{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Regression - Introduction (Part 1)\n",
    "\n",
    "Regression is one of the most common and basic supervised learning tasks in machine learning. Suppose we're given a dataset $\\mathcal{D}$ of the form\n",
    "\n",
    "$$ \\mathcal{D}  = \\{ (X_i, y_i) \\} \\qquad \\text{for}\\qquad i=1,2,...,N$$\n",
    "\n",
    "The goal of linear regression is to fit a function to the data of the form:\n",
    "\n",
    "$$ y = w X + b + \\epsilon $$\n",
    "\n",
    "where $w$ and $b$ are learnable parameters and $\\epsilon$ represents observation noise. Specifically $w$ is a matrix of weights and $b$ is a bias vector.\n",
    "\n",
    "In this tutorial, we will first implement linear regression in PyTorch and learn point estimates for the parameters $w$ and $b$.  Then we will see how to incorporate uncertainty into our estimates by using Pyro to implement Bayesian regression. Additionally, we will learn how to use the Pyro's utility functions to do predictions and serve our model using `TorchScript`.\n",
    "\n",
    "## Tutorial Outline\n",
    "\n",
    " - [Setup](#Setup)\n",
    "   - [Dataset](#Dataset)\n",
    " - [Linear Regression](#Linear-Regression)\n",
    "   - [Training with PyTorch Optimizers](#Training-with-PyTorch-Optimizers)\n",
    "   - [Regression Fit](#Plotting-the-Regression-Fit)\n",
    " - [Bayesian Regression with Pyro's SVI](#Bayesian-Regression-with-Pyro's-Stochastic-Variational-Inference-%28SVI%29)\n",
    "   - [Model](#Model)\n",
    "   - [Using an AutoGuide](#Using-an-AutoGuide)\n",
    "   - [Optimizing the Evidence Lower Bound](#Optimizing-the-Evidence-Lower-Bound)\n",
    " - [Model Evaluation](#Model-Evaluation)\n",
    " - [Serving the Model using TorchScript](#Model-Serving-via-TorchScript)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "Let's begin by importing the modules we'll need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -s -f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from functools import partial\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pyro\n",
    "import pyro.distributions as dist\n",
    "\n",
    "# for CI testing\n",
    "smoke_test = ('CI' in os.environ)\n",
    "assert pyro.__version__.startswith('1.9.1')\n",
    "pyro.set_rng_seed(1)\n",
    "\n",
    "\n",
    "# Set matplotlib settings\n",
    "%matplotlib inline\n",
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset \n",
    "\n",
    "The following example is adapted from \\[1\\].  We would like to explore the relationship between topographic heterogeneity of a nation as measured by the Terrain Ruggedness Index (variable *rugged* in the dataset) and its GDP per capita. In particular, it was noted by the authors in \\[2\\] that terrain ruggedness or bad geography is related to poorer economic performance outside of Africa, but rugged terrains have had a reverse effect on income for African nations. Let us look at the data and investigate this relationship.  We will be focusing on three features from the dataset:\n",
    "\n",
    "  - `rugged`: quantifies the Terrain Ruggedness Index\n",
    "  - `cont_africa`: whether the given nation is in Africa\n",
    "  - `rgdppc_2000`: Real GDP per capita for the year 2000\n",
    "  \n",
    "The response variable GDP is highly skewed, so we will log-transform it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_URL = \"https://github.com/pyro-ppl/datasets/blob/master/rugged_data.csv?raw=true\"\n",
    "data = pd.read_csv(DATA_URL, encoding=\"ISO-8859-1\")\n",
    "df = data[[\"cont_africa\", \"rugged\", \"rgdppc_2000\"]]\n",
    "df = df[np.isfinite(df.rgdppc_2000)]\n",
    "df[\"rgdppc_2000\"] = np.log(df[\"rgdppc_2000\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "african_nations = df[df[\"cont_africa\"] == 1]\n",
    "non_african_nations = df[df[\"cont_africa\"] == 0]\n",
    "sns.scatterplot(x=non_african_nations[\"rugged\"],\n",
    "                y=non_african_nations[\"rgdppc_2000\"],\n",
    "                ax=ax[0])\n",
    "ax[0].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"Non African Nations\")\n",
    "sns.scatterplot(x=african_nations[\"rugged\"],\n",
    "                y=african_nations[\"rgdppc_2000\"],\n",
    "                ax=ax[1])\n",
    "ax[1].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"African Nations\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "We would like to predict log GDP per capita of a nation as a function of two features from the dataset - whether the nation is in Africa, and its Terrain Ruggedness Index. We will create a trivial class called `PyroModule[nn.Linear]` that subclasses [PyroModule](http://docs.pyro.ai/en/dev/nn.html#module-pyro.nn.module) and `torch.nn.Linear`. `PyroModule` is very similar to PyTorch's `nn.Module`, but additionally supports [Pyro primitives](http://docs.pyro.ai/en/dev/primitives.html#primitives) as attributes that can be modified by Pyro's [effect handlers](http://pyro.ai/examples/effect_handlers.html) (see the [next section](#Model) on how we can have module attributes that are `pyro.sample` primitives). Some general notes:\n",
    "\n",
    "  - Learnable parameters in PyTorch modules are instances of `nn.Parameter`, in this case the `weight` and `bias` parameters of the `nn.Linear` class. When declared inside a `PyroModule` as attributes, these are automatically registered in Pyro's param store. While this model does not require us to constrain the value of these parameters during optimization, this can also be easily achieved in `PyroModule` using the [PyroParam](http://docs.pyro.ai/en/dev/nn.html#pyro.nn.module.PyroParam) statement. \n",
    "  - Note that while the `forward` method of `PyroModule[nn.Linear]` inherits from `nn.Linear`, it can also be easily overridden. e.g. in the case of logistic regression, we apply a sigmoid transformation to the linear predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from pyro.nn import PyroModule\n",
    "\n",
    "assert issubclass(PyroModule[nn.Linear], nn.Linear)\n",
    "assert issubclass(PyroModule[nn.Linear], PyroModule)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training with PyTorch Optimizers\n",
    "\n",
    "Note that in addition to the two features `rugged` and `cont_africa`, we also include an interaction term in our model, which lets us separately model the effect of ruggedness on the GDP for nations within and outside Africa.\n",
    "\n",
    "We use the mean squared error (MSE) as our loss and Adam as our optimizer from the `torch.optim` module. We would like to optimize the parameters of our model, namely the `weight` and `bias` parameters of the network, which corresponds to our regression coefficents and the intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[iteration 0050] loss: 3179.7852\n",
      "[iteration 0100] loss: 1616.1371\n",
      "[iteration 0150] loss: 1109.4117\n",
      "[iteration 0200] loss: 833.7545\n",
      "[iteration 0250] loss: 637.5822\n",
      "[iteration 0300] loss: 488.2652\n",
      "[iteration 0350] loss: 376.4650\n",
      "[iteration 0400] loss: 296.0483\n",
      "[iteration 0450] loss: 240.6140\n",
      "[iteration 0500] loss: 203.9386\n",
      "[iteration 0550] loss: 180.6171\n",
      "[iteration 0600] loss: 166.3493\n",
      "[iteration 0650] loss: 157.9457\n",
      "[iteration 0700] loss: 153.1786\n",
      "[iteration 0750] loss: 150.5735\n",
      "[iteration 0800] loss: 149.2020\n",
      "[iteration 0850] loss: 148.5065\n",
      "[iteration 0900] loss: 148.1668\n",
      "[iteration 0950] loss: 148.0070\n",
      "[iteration 1000] loss: 147.9347\n",
      "[iteration 1050] loss: 147.9032\n",
      "[iteration 1100] loss: 147.8900\n",
      "[iteration 1150] loss: 147.8847\n",
      "[iteration 1200] loss: 147.8827\n",
      "[iteration 1250] loss: 147.8819\n",
      "[iteration 1300] loss: 147.8817\n",
      "[iteration 1350] loss: 147.8816\n",
      "[iteration 1400] loss: 147.8815\n",
      "[iteration 1450] loss: 147.8815\n",
      "[iteration 1500] loss: 147.8815\n",
      "Learned parameters:\n",
      "weight [[-1.9478593  -0.20278624  0.39330274]]\n",
      "bias [9.22308]\n"
     ]
    }
   ],
   "source": [
    "# Dataset: Add a feature to capture the interaction between \"cont_africa\" and \"rugged\"\n",
    "df[\"cont_africa_x_rugged\"] = df[\"cont_africa\"] * df[\"rugged\"]\n",
    "data = torch.tensor(df[[\"cont_africa\", \"rugged\", \"cont_africa_x_rugged\", \"rgdppc_2000\"]].values,\n",
    "                        dtype=torch.float)\n",
    "x_data, y_data = data[:, :-1], data[:, -1]\n",
    "\n",
    "# Regression model\n",
    "linear_reg_model = PyroModule[nn.Linear](3, 1)\n",
    "\n",
    "# Define loss and optimize\n",
    "loss_fn = torch.nn.MSELoss(reduction='sum')\n",
    "optim = torch.optim.Adam(linear_reg_model.parameters(), lr=0.05)\n",
    "num_iterations = 1500 if not smoke_test else 2\n",
    "\n",
    "def train():\n",
    "    # run the model forward on the data\n",
    "    y_pred = linear_reg_model(x_data).squeeze(-1)\n",
    "    # calculate the mse loss\n",
    "    loss = loss_fn(y_pred, y_data)\n",
    "    # initialize gradients to zero\n",
    "    optim.zero_grad()\n",
    "    # backpropagate\n",
    "    loss.backward()\n",
    "    # take a gradient step\n",
    "    optim.step()\n",
    "    return loss\n",
    "\n",
    "for j in range(num_iterations):\n",
    "    loss = train()\n",
    "    if (j + 1) % 50 == 0:\n",
    "        print(\"[iteration %04d] loss: %.4f\" % (j + 1, loss.item()))\n",
    "\n",
    "            \n",
    "# Inspect learned parameters\n",
    "print(\"Learned parameters:\")\n",
    "for name, param in linear_reg_model.named_parameters():\n",
    "    print(name, param.data.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Regression Fit\n",
    "\n",
    "Let us plot the regression fit for our model, separately for countries outside and within Africa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit = df.copy()\n",
    "fit[\"mean\"] = linear_reg_model(x_data).detach().cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "african_nations = fit[fit[\"cont_africa\"] == 1]\n",
    "non_african_nations = fit[fit[\"cont_africa\"] == 0]\n",
    "fig.suptitle(\"Regression Fit\", fontsize=16)\n",
    "ax[0].plot(non_african_nations[\"rugged\"], non_african_nations[\"rgdppc_2000\"], \"o\")\n",
    "ax[0].plot(non_african_nations[\"rugged\"], non_african_nations[\"mean\"], linewidth=2)\n",
    "ax[0].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"Non African Nations\")\n",
    "ax[1].plot(african_nations[\"rugged\"], african_nations[\"rgdppc_2000\"], \"o\")\n",
    "ax[1].plot(african_nations[\"rugged\"], african_nations[\"mean\"], linewidth=2)\n",
    "ax[1].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"African Nations\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that the relationship between terrain ruggedness has an inverse relationship with GDP for non-African nations, but it positively affects the GDP for African nations. It is however unclear how robust this trend is. In particular, we would like to understand how the regression fit would vary due to parameter uncertainty. To address this, we will build a simple Bayesian model for linear regression. [Bayesian modeling](http://mlg.eng.cam.ac.uk/zoubin/papers/NatureReprint15.pdf) offers a systematic framework for reasoning about model uncertainty. Instead of just learning point estimates, we're going to learn a _distribution_ over parameters that are consistent with the observed data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Regression with Pyro's Stochastic Variational Inference (SVI)\n",
    "\n",
    "### Model\n",
    "\n",
    "In order to make our linear regression Bayesian, we need to put priors on the parameters $w$ and $b$. These are distributions that represent our prior belief about reasonable values for $w$ and $b$ (before observing any data).\n",
    "\n",
    "Making a Bayesian model for linear regression is very intuitive using `PyroModule` as earlier. Note the following:\n",
    "\n",
    " - The `BayesianRegression` module internally uses the same `PyroModule[nn.Linear]` module. However, note that we replace the `weight` and the `bias` of the this module with `PyroSample` statements. These statements allow us to place a prior over the `weight` and `bias` parameters, instead of treating them as fixed learnable parameters. For the bias component, we set a reasonably wide prior since it is likely to be substantially above 0.\n",
    " - The `BayesianRegression.forward` method specifies the generative process. We generate the mean value of the response by calling the `linear` module (which, as you saw, samples the `weight` and `bias` parameters from the prior and returns a value for the mean response). Finally we use the `obs` argument to the `pyro.sample` statement to condition on the observed data `y_data` with a learned observation noise `sigma`. The model returns the regression line given by the variable `mean`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyro.nn import PyroSample\n",
    "\n",
    "\n",
    "class BayesianRegression(PyroModule):\n",
    "    def __init__(self, in_features, out_features):\n",
    "        super().__init__()\n",
    "        self.linear = PyroModule[nn.Linear](in_features, out_features)\n",
    "        self.linear.weight = PyroSample(dist.Normal(0., 1.).expand([out_features, in_features]).to_event(2))\n",
    "        self.linear.bias = PyroSample(dist.Normal(0., 10.).expand([out_features]).to_event(1))\n",
    "        \n",
    "    def forward(self, x, y=None):\n",
    "        sigma = pyro.sample(\"sigma\", dist.Uniform(0., 10.))\n",
    "        mean = self.linear(x).squeeze(-1)\n",
    "        with pyro.plate(\"data\", x.shape[0]):\n",
    "            obs = pyro.sample(\"obs\", dist.Normal(mean, sigma), obs=y)\n",
    "        return mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using an AutoGuide\n",
    "\n",
    "In order to do inference, i.e. learn the posterior distribution over our unobserved parameters, we will use Stochastic Variational Inference (SVI). The guide determines a family of distributions, and `SVI` aims to find an approximate posterior distribution from this family that has the lowest KL divergence from the true posterior. \n",
    "\n",
    "Users can write arbitrarily flexible custom guides in Pyro, but in this tutorial, we will restrict ourselves to Pyro's [autoguide library](http://docs.pyro.ai/en/dev/infer.autoguide.html). In the next [tutorial](bayesian_regression_ii.ipynb), we will explore how to write guides by hand.\n",
    "\n",
    "To begin with, we will use the `AutoDiagonalNormal` guide that models the distribution of unobserved parameters in the model as a Gaussian with diagonal covariance, i.e. it assumes that there is no correlation amongst the latent variables (quite a strong modeling assumption as we shall see in [Part II](bayesian_regression_ii.ipynb)). Under the hood, this defines a `guide` that uses a `Normal` distribution with learnable parameters corresponding to each `sample` statement in the model. e.g. in our case, this distribution should have a size of `(5,)` correspoding to the 3 regression coefficients for each of the terms, and 1 component contributed each by the intercept term and `sigma` in the model. \n",
    "\n",
    "Autoguide also supports learning MAP estimates with `AutoDelta` or composing guides with `AutoGuideList` (see the [docs](http://docs.pyro.ai/en/dev/infer.autoguide.html) for more information)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyro.infer.autoguide import AutoDiagonalNormal\n",
    "\n",
    "model = BayesianRegression(3, 1)\n",
    "guide = AutoDiagonalNormal(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizing the Evidence Lower Bound\n",
    "\n",
    "We will use stochastic variational inference (SVI) (for an introduction to SVI, see [SVI Part I](svi_part_i.ipynb)) for doing inference. Just like in the non-Bayesian linear regression model, each iteration of our training loop will take a gradient step, with the difference that in this case, we'll use the Evidence Lower Bound (ELBO) objective instead of the MSE loss by constructing a `Trace_ELBO` object that we pass to `SVI`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyro.infer import SVI, Trace_ELBO\n",
    "\n",
    "\n",
    "adam = pyro.optim.Adam({\"lr\": 0.03})\n",
    "svi = SVI(model, guide, adam, loss=Trace_ELBO())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we use the `Adam` optimizer from Pyro's `optim` module and not the `torch.optim` module as earlier. Here `Adam` is a thin wrapper around `torch.optim.Adam` (see [here](svi_part_i.ipynb#Optimizers) for a discussion). Optimizers in `pyro.optim` are used to optimize and update parameter values in Pyro's parameter store. In particular, you will notice that we do not need to pass in learnable parameters to the optimizer since that is determined by the guide code and happens behind the scenes within the `SVI` class automatically. To take an ELBO gradient step we simply call the step method of SVI. The data argument we pass to `SVI.step` will be passed to both `model()` and `guide()`. The complete training loop is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[iteration 0001] loss: 6.2310\n",
      "[iteration 0101] loss: 3.5253\n",
      "[iteration 0201] loss: 3.2347\n",
      "[iteration 0301] loss: 3.0890\n",
      "[iteration 0401] loss: 2.6377\n",
      "[iteration 0501] loss: 2.0626\n",
      "[iteration 0601] loss: 1.4852\n",
      "[iteration 0701] loss: 1.4631\n",
      "[iteration 0801] loss: 1.4632\n",
      "[iteration 0901] loss: 1.4592\n",
      "[iteration 1001] loss: 1.4940\n",
      "[iteration 1101] loss: 1.4988\n",
      "[iteration 1201] loss: 1.4938\n",
      "[iteration 1301] loss: 1.4679\n",
      "[iteration 1401] loss: 1.4581\n"
     ]
    }
   ],
   "source": [
    "pyro.clear_param_store()\n",
    "for j in range(num_iterations):\n",
    "    # calculate the loss and take a gradient step\n",
    "    loss = svi.step(x_data, y_data)\n",
    "    if j % 100 == 0:\n",
    "        print(\"[iteration %04d] loss: %.4f\" % (j + 1, loss / len(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can examine the optimized parameter values by fetching from Pyro's param store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoDiagonalNormal.loc Parameter containing:\n",
      "tensor([-2.2371, -1.8097, -0.1691,  0.3791,  9.1823])\n",
      "AutoDiagonalNormal.scale tensor([0.0551, 0.1142, 0.0387, 0.0769, 0.0702])\n"
     ]
    }
   ],
   "source": [
    "guide.requires_grad_(False)\n",
    "\n",
    "for name, value in pyro.get_param_store().items():\n",
    "    print(name, pyro.param(name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, instead of just point estimates, we now have uncertainty estimates (`AutoDiagonalNormal.scale`) for our learned parameters. Note that Autoguide packs the latent variables into a single tensor, in this case, one entry per variable sampled in our model. Both the `loc` and `scale` parameters have size `(5,)`, one for each of the latent variables in the model, as we had remarked earlier.\n",
    "\n",
    "To look at the distribution of the latent parameters more clearly, we can make use of the `AutoDiagonalNormal.quantiles` method which will unpack the latent samples from the autoguide, and automatically constrain them to the site's support (e.g. the variable `sigma` must lie in `(0, 10)`). We see that the median values for the parameters are quite close to the Maximum Likelihood point estimates we obtained from our first model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sigma': [tensor(0.9328), tensor(0.9647), tensor(0.9976)],\n",
       " 'linear.weight': [tensor([[-1.8868, -0.1952,  0.3272]]),\n",
       "  tensor([[-1.8097, -0.1691,  0.3791]]),\n",
       "  tensor([[-1.7327, -0.1429,  0.4309]])],\n",
       " 'linear.bias': [tensor([9.1350]), tensor([9.1823]), tensor([9.2297])]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guide.quantiles([0.25, 0.5, 0.75])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "To evaluate our model, we'll generate some predictive samples and look at the posteriors. For this we will make use of the [Predictive](http://docs.pyro.ai/en/stable/inference_algos.html#pyro.infer.predictive.Predictive) utility class.\n",
    "\n",
    "  - We generate 800 samples from our trained model. Internally, this is done by first generating samples for the unobserved sites in the `guide`, and then running the model forward by conditioning the sites to values sampled from the `guide`. Refer to the [Model Serving](#Model-Serving-via-TorchScript) section for insight on how the `Predictive` class works.\n",
    "  - Note that in `return_sites`, we specify both the outcome (`\"obs\"` site) as well as the return value of the model (`\"_RETURN\"`) which captures the regression line. Additionally, we would also like to capture the regression coefficients (given by `\"linear.weight\"`) for further analysis.\n",
    "  - The remaining code is simply used to plot the 90% CI for the two variables from our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyro.infer import Predictive\n",
    "\n",
    "\n",
    "def summary(samples):\n",
    "    site_stats = {}\n",
    "    for k, v in samples.items():\n",
    "        site_stats[k] = {\n",
    "            \"mean\": torch.mean(v, 0),\n",
    "            \"std\": torch.std(v, 0),\n",
    "            \"5%\": v.kthvalue(int(len(v) * 0.05), dim=0)[0],\n",
    "            \"95%\": v.kthvalue(int(len(v) * 0.95), dim=0)[0],\n",
    "        }\n",
    "    return site_stats\n",
    "\n",
    "\n",
    "predictive = Predictive(model, guide=guide, num_samples=800, \n",
    "                        return_sites=(\"linear.weight\", \"obs\", \"_RETURN\"))\n",
    "samples = predictive(x_data)\n",
    "pred_summary = summary(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = pred_summary[\"_RETURN\"]\n",
    "y = pred_summary[\"obs\"]\n",
    "predictions = pd.DataFrame({\n",
    "    \"cont_africa\": x_data[:, 0],\n",
    "    \"rugged\": x_data[:, 1],\n",
    "    \"mu_mean\": mu[\"mean\"],\n",
    "    \"mu_perc_5\": mu[\"5%\"],\n",
    "    \"mu_perc_95\": mu[\"95%\"],\n",
    "    \"y_mean\": y[\"mean\"],\n",
    "    \"y_perc_5\": y[\"5%\"],\n",
    "    \"y_perc_95\": y[\"95%\"],\n",
    "    \"true_gdp\": y_data,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "african_nations = predictions[predictions[\"cont_africa\"] == 1]\n",
    "non_african_nations = predictions[predictions[\"cont_africa\"] == 0]\n",
    "african_nations = african_nations.sort_values(by=[\"rugged\"])\n",
    "non_african_nations = non_african_nations.sort_values(by=[\"rugged\"])\n",
    "fig.suptitle(\"Regression line 90% CI\", fontsize=16)\n",
    "ax[0].plot(non_african_nations[\"rugged\"],\n",
    "           non_african_nations[\"mu_mean\"])\n",
    "ax[0].fill_between(non_african_nations[\"rugged\"], \n",
    "                   non_african_nations[\"mu_perc_5\"],\n",
    "                   non_african_nations[\"mu_perc_95\"],\n",
    "                   alpha=0.5)\n",
    "ax[0].plot(non_african_nations[\"rugged\"], \n",
    "           non_african_nations[\"true_gdp\"],\n",
    "           \"o\")\n",
    "ax[0].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"Non African Nations\")\n",
    "idx = np.argsort(african_nations[\"rugged\"])\n",
    "ax[1].plot(african_nations[\"rugged\"], \n",
    "           african_nations[\"mu_mean\"])\n",
    "ax[1].fill_between(african_nations[\"rugged\"],\n",
    "                   african_nations[\"mu_perc_5\"],\n",
    "                   african_nations[\"mu_perc_95\"],\n",
    "                   alpha=0.5)\n",
    "ax[1].plot(african_nations[\"rugged\"], \n",
    "           african_nations[\"true_gdp\"],\n",
    "           \"o\")\n",
    "ax[1].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"African Nations\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure shows the uncertainty in our estimate of the regression line, and the 90% CI around the mean. We can also see that most of the data points actually lie outside the 90% CI, and this is expected because we have not plotted the outcome variable which will be affected by `sigma`! Let us do so next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "fig.suptitle(\"Posterior predictive distribution with 90% CI\", fontsize=16)\n",
    "ax[0].plot(non_african_nations[\"rugged\"], \n",
    "           non_african_nations[\"y_mean\"])\n",
    "ax[0].fill_between(non_african_nations[\"rugged\"], \n",
    "                   non_african_nations[\"y_perc_5\"],\n",
    "                   non_african_nations[\"y_perc_95\"],\n",
    "                   alpha=0.5)\n",
    "ax[0].plot(non_african_nations[\"rugged\"], \n",
    "           non_african_nations[\"true_gdp\"],\n",
    "           \"o\")\n",
    "ax[0].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"Non African Nations\")\n",
    "idx = np.argsort(african_nations[\"rugged\"])\n",
    "\n",
    "ax[1].plot(african_nations[\"rugged\"], \n",
    "           african_nations[\"y_mean\"])\n",
    "ax[1].fill_between(african_nations[\"rugged\"],\n",
    "                   african_nations[\"y_perc_5\"],\n",
    "                   african_nations[\"y_perc_95\"],\n",
    "                   alpha=0.5)\n",
    "ax[1].plot(african_nations[\"rugged\"], \n",
    "           african_nations[\"true_gdp\"],\n",
    "           \"o\")\n",
    "ax[1].set(xlabel=\"Terrain Ruggedness Index\",\n",
    "          ylabel=\"log GDP (2000)\",\n",
    "          title=\"African Nations\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the outcome from our model and the 90% CI accounts for the majority of the data points that we observe in practice. It is usually a good idea to do such posterior predictive checks to see if our model gives valid predictions. \n",
    "\n",
    "Finally, let us revisit our earlier question of how robust the relationship between terrain ruggedness and GDP is against any uncertainty in the parameter estimates from our model. For this, we plot the distribution of the slope of the log GDP given terrain ruggedness for nations within and outside Africa. As can be seen below, the probability mass for African nations is largely concentrated in the positive region and vice-versa for other nations, lending further credence to the original hypothesis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight = samples[\"linear.weight\"]\n",
    "weight = weight.reshape(weight.shape[0], 3)\n",
    "gamma_within_africa = weight[:, 1] + weight[:, 2]\n",
    "gamma_outside_africa = weight[:, 1]\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "sns.distplot(gamma_within_africa, kde_kws={\"label\": \"African nations\"},)\n",
    "sns.distplot(gamma_outside_africa, kde_kws={\"label\": \"Non-African nations\"})\n",
    "fig.suptitle(\"Density of Slope : log(GDP) vs. Terrain Ruggedness\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Serving via TorchScript\n",
    "\n",
    "Finally, note that the `model`, `guide` and the `Predictive` utility class are all `torch.nn.Module` instances, and can be serialized as [TorchScript](https://pytorch.org/docs/stable/jit.html). \n",
    "\n",
    "Here, we show how we can serve a Pyro model as a [torch.jit.ModuleScript](https://pytorch.org/docs/stable/jit.html#torch.jit.ScriptModule), which can be run separately as a C++ program without a Python runtime.\n",
    "\n",
    "To do so, we will rewrite our own simple version of the `Predictive` utility class using Pyro's [effect handling library](http://pyro.ai/examples/effect_handlers.html). This uses:\n",
    "\n",
    " - the `trace` poutine to capture the execution trace from running the model/guide code.\n",
    " - the `replay` poutine to condition the sites in the model to values sampled from the guide trace.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from pyro import poutine\n",
    "from pyro.poutine.util import prune_subsample_sites\n",
    "import warnings\n",
    "\n",
    "\n",
    "class Predict(torch.nn.Module):\n",
    "    def __init__(self, model, guide):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.guide = guide\n",
    "        \n",
    "    def forward(self, *args, **kwargs):\n",
    "        samples = {}\n",
    "        guide_trace = poutine.trace(self.guide).get_trace(*args, **kwargs)\n",
    "        model_trace = poutine.trace(poutine.replay(self.model, guide_trace)).get_trace(*args, **kwargs)\n",
    "        for site in prune_subsample_sites(model_trace).stochastic_nodes:\n",
    "            samples[site] = model_trace.nodes[site]['value']\n",
    "        return tuple(v for _, v in sorted(samples.items()))\n",
    "\n",
    "predict_fn = Predict(model, guide)\n",
    "predict_module = torch.jit.trace_module(predict_fn, {\"forward\": (x_data,)}, check_trace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use [torch.jit.trace_module](https://pytorch.org/docs/stable/jit.html#torch.jit.trace_module) to trace the `forward` method of this module and save it using [torch.jit.save](https://pytorch.org/docs/stable/jit.html#torch.jit.save). This saved model `reg_predict.pt` can be loaded with PyTorch's C++ API using `torch::jit::load(filename)`, or using the Python API as we do below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([9.2165]),\n",
       " tensor([[-1.6612, -0.1498,  0.4282]]),\n",
       " tensor([ 7.5951,  8.2473,  9.3864,  9.2590,  9.0540,  9.3915,  8.6764,  9.3775,\n",
       "          9.5473,  9.6144, 10.3521,  8.5452,  5.4008,  8.4601,  9.6219,  9.7774,\n",
       "          7.1958,  7.2581,  8.9159,  9.0875,  8.3730,  8.7903,  9.3167,  8.8155,\n",
       "          7.4433,  9.9981,  8.6909,  9.2915, 10.1376,  7.7618, 10.1916,  7.4754,\n",
       "          6.3473,  7.7584,  9.1307,  6.0794,  8.5641,  7.8487,  9.2828,  9.0763,\n",
       "          7.9250, 10.9226,  8.0005, 10.1799,  5.3611,  8.1174,  8.0585,  8.5098,\n",
       "          6.8656,  8.6765,  7.8925,  9.5233, 10.1269, 10.2661,  7.8883,  8.9194,\n",
       "         10.2866,  7.0821,  8.2370,  8.3087,  7.8408,  8.4891,  8.0107,  7.6815,\n",
       "          8.7497,  9.3551,  9.9687, 10.4804,  8.5176,  7.1679, 10.8805,  7.4919,\n",
       "          8.7088,  9.2417,  9.2360,  9.7907,  8.4934,  7.8897,  9.5338,  9.6572,\n",
       "          9.6604,  9.9855,  6.7415,  8.1721, 10.0646, 10.0817,  8.4503,  9.2588,\n",
       "          8.4489,  7.7516,  6.8496,  9.2208,  8.9852, 10.6585,  9.4218,  9.1290,\n",
       "          9.5631,  9.7422, 10.2814,  7.2624,  9.6727,  8.9743,  6.9666,  9.5856,\n",
       "          9.2518,  8.4207,  8.6988,  9.1914,  7.8161,  9.8446,  6.5528,  8.5518,\n",
       "          6.7168,  7.0694,  8.9211,  8.5311,  8.4545, 10.8346,  7.8768,  9.2537,\n",
       "          9.0776,  9.4698,  7.9611,  9.2177,  8.0880,  8.5090,  9.2262,  8.9242,\n",
       "          9.3966,  7.5051,  9.1014,  8.9601,  7.7225,  8.7569,  8.5847,  8.8465,\n",
       "          9.7494,  8.8587,  6.5624,  6.9372,  9.9806, 10.1259,  9.1864,  7.5758,\n",
       "          9.8258,  8.6375,  7.6954,  8.9718,  7.0985,  8.6360,  8.5951,  8.9163,\n",
       "          8.4661,  8.4551, 10.6844,  7.5948,  8.7568,  9.5296,  8.9530,  7.1214,\n",
       "          9.1401,  8.4992,  8.9115, 10.9739,  8.1593, 10.1162,  9.7072,  7.8641,\n",
       "          8.8606,  7.5935]),\n",
       " tensor(0.9631))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.jit.save(predict_module, '/tmp/reg_predict.pt')\n",
    "pred_loaded = torch.jit.load('/tmp/reg_predict.pt')\n",
    "pred_loaded(x_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check that our `Predict` module was indeed serialized correctly, by generating samples from the loaded module and regenerating the previous plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gACDOnj0rlwUFBYmqVavKPx85ckQAEDt37lTsu2fPHp3luT0P+XneJk6cKExMTOSfz507JwCI4OBgRb3g4GABQJw5c0YIIURMTIwwMDAQPXr0UNSbNGmS1rEXL14sAGhdG8OGDRMAFL9vIdR/GwkJCfLXo0eP5G2a62ncuHG5ntMnn3wiAIjr168rno9Zs2aJhIQEER8fL0JDQ0XNmjUFALF//37F/seOHRMAxE8//ZTrMYQQYsyYMcLU1FTrtczS0lJ+ncrOzhZmZmZ59vd1bdq0Seta0vjzzz+FJEliyZIlivKzZ89qlXt5eQkAYsuWLVrt2NjYCADi3LlzWttyvm5nZ2eLRo0aibp16yrKvby8REBAgPzz7t27BQDx3nvviczMTLl8+vTpAoCIiYnJ87y/++47AUBcuHBBJCQkiJiYGLF582ZhY2MjrKysFK+dT548EQDEd999p/PcRo0aJf/ct29frdewf/75R5ibmwsA4smTJ0IIIW7duiUkSRL9+/dXtDd69GgBQNHmuHHjhJ2dnfj7778VdYcOHSpMTU3la3v9+vUCgFi5cqVc58WLF8Lb21sAELt375bLAwICBACxcOFCuSwrK0tUqlRJ+Pr6ymU///yzzut7y5YtinLN//Ocf58v69evn3B1ddX6/0BUnDhdjd4aBw4cgJOTk+JTJWNjY3mo/OV59i9/2vrkyRMkJiaiadOm+P333xXtAdAaQs85NUAIgZ07d6JDhw4QQuDhw4fyl5+fHxITE+V2Dxw4AGdnZ/mTSAAwNzfHkCFDCuEZ+J8DBw7Azc0NXbp0kctUKhVGjhyJp0+f4syZM4r6QUFBWp9G7ty5Ey4uLjr7lnNRb69evRSfPmqmcdy+fVsuGzZsGPbt24emTZvixIkTmD59Oho3boyqVaviwoULiuMaGxtj8uTJrzxuq1atcv3kW5eci8NHjhwJIQQOHjyY7zZyU69ePVStWlUeefjhhx/QtWtXnesDfvnlF2RlZWHMmDGKcxo+fDjMzMywf/9+AMDZs2fx5MkTeYRN4+OPP9a5fuF1bd++Hfb29mjWrJni+m3UqBFUKhV+/fXXPPevVq0ahBCvNYqj+TsbO3asonzcuHEAID8XR44cQXZ2ttYnzSNHjtRq89GjRwC0P2HXTL2ztLRUlO/atQv29vbyV35G716maS/nVMNJkybB3t4eTk5O8PX1xb179zB//nz4+/sr6mn6+fDhwzyPExQUhLS0NOzdu1cu279/P5KTk+XRHUmSYG1tjbNnz2pNXSxKO3bsgLGxMQICAhTXkKenJ5ydnbWuobJly6J79+4626pTpw4aNmyoVf7y6/bTp0/x6NEjNG/eHJcuXUJ2dvYr+zh48GDFiKTmdSrn9MHcvPfee7C3t0fFihXRu3dvODk5Yf/+/a89knPo0CG0bt1a8Rrm4uKCrl27KuodPnwYQoh8Xfvbt29H69atYWJiovg9tG7dGmlpaTh79iwA9d+dtbU1+vfvL++rUqlyHckxMDDAxx9/rPi5SZMmitf47du3w9XVFe+9957i2M2aNYOhoaF8DWhGoPfu3YusrCydx7O1tcXDhw9x6tQpnduJigOnq9FbIzo6GpUrV4aBgTL21mQbe3kdyb59+zBjxgxEREQoprq8/IYzOjoaBgYGiixCABQLywH1/OenT59i5cqVWLlypc6+aeakR0dHw9PTU+vNes4231R0dDSqVKmidRxdzwUAeHh4aLURFRWF6tWr5ytr0MtTTYD/vWl78uSJorxdu3Zo164dnj9/jvDwcPzwww9YvXo12rdvj+vXr8POzg5RUVGoWLGiznUsOenqd14qV66s+FnzZlYz7eRN9ezZE6tWrcLgwYNx8eJFeZ1BTprnP+fvXZNWW7Nd85iz35r5+4Xl5s2bSEhI0Fqkr1EYiSFyEx0dDZVKBXd3d0W5u7s7zMzMtJ6LnEGti4tLrgGfyLFeQHNN5Vwf0KJFCxw5cgQA8PXXXxd47YqmvZzXbHBwMAICAmBgYAA7OzvUqFFDZ9Cr6eerMoI1bNgQ7u7u8tRTQD1VzcXFBR988IFcTzMNy9XVFfXr14e/vz8++ugjree4MN28eRPp6em5ThnNeQ25ublpvVZr5PZ3HRoaiunTpyM8PFzr3kfJyck6p3m9LL+vU7nZsGEDXFxc8PjxYyxfvhwREREFSojwstTUVCQkJOj8kCZnWW7Xvru7u+LDj/T0dMTExCAmJkbn2i1A+b/Izc1NK8FMbv+LHBwctBK62NnZKZ67mzdv4u+//37l60j79u3RunVrjBs3DjNmzMCHH36ITp06oUePHvL5jBkzBj///DOaN28ONzc3+Pn5ISgoSF7XRlQcGORQqXPq1Cl07NgRzZo1w9KlS+Hs7AxjY2OsW7dOa+F4fmg+QezTpw/69euns07t2rXfqM9F7XX/UWvkFgjlfJOpYWFhgebNm6N58+aws7PD7Nmz8csvv2itNXiVN+13YevVqxemTZuG4cOHw8XFBT4+PsV27NzeIOf2SenLsrOz4erqinXr1unc7ujo+EZ9K26aNS9PnjxR3J9Is7bjypUrijdzjo6O8jmuXr26wEHOlStXoFKptN5EV61aVSv5gS6aN4r5uZdSUFAQ5s+fj8TERBgZGeHAgQP4+OOPFQFDnz594OPjg927d+PIkSOYNWsWZs+ejb1792plgCss2dnZMDc3zzWBQs71Y3n97eraFhERAT8/P9SvXx8LFy5E+fLlYWxsjO3bt2PlypX5Gskp6OtUTo0bN5YDjYCAANSrVw89e/bEX3/9JQeveQWq+flbfBOa56Bz5865jshoUpcXVH6eu+zsbLzzzjtYsWKFzrqatZhGRkY4dOgQTp06hf379+PQoUPo27cvFi1ahJMnT8LExARubm64cuUKDh48iEOHDuHAgQNYuXIlRo0apTOpA1FRYJBDbw03Nzf8+eefyM7OVvzDv3btmrwdUE+HMjU1xeHDh2FiYiLXy/kGz83NDdnZ2YiKilK8Icp58z9N5rWsrKxXvqHRvHALIRT/DHXdUPBNuLm54caNG1rlOZ+LvFSqVAmRkZHIysoq0ntA1K9fHwAQFxcnHzcsLAxJSUmv/GS2oG7evKlIeqB5jl7+hPtN7q9RuXJl1K9fH8ePH8eYMWNy/aRa8/xfv34dLi4ucnlaWhpiYmJQr149Rb2bN28q0gunpaUhOjpa8aZa86n006dP4eTkJJfryoSXU6VKlXD+/Hk0a9asyNLv5sbNzQ3p6em4e/eu4hP86OhopKamys+B5vHWrVuK32FsbCxSUlIUbWqCmTt37ihGwdq1a4cxY8Zg8+bNWlOCXtetW7dw9uxZfPjhh6+dulwzXSo/97gKCgrC7NmzsWfPHpiYmCAlJQU9evTQqufq6oqRI0di5MiRiIuLg7e3N2bOnFlkQU6lSpWQkpKC2rVra2VLLAx79uyBEAKHDh1SBEy7d+8u9GPlh4mJCWbMmIHOnTtj7dq1cnY/KysrGBkZKTKjAcDjx48VI4hmZmawt7fHrVu3tNrOWfbytd+gQQO5/O7du4rEA6ampnBxcUFaWlq+/hcdOnQIGRkZitGcN/lfpPmf4ePjozOL5sskSUKzZs3QrFkzzJ49G0uXLsWIESPw888/y9O5zczM0KVLF3Tp0gWZmZn46KOPsGDBAkyePJk3V6ZiwTU59Nbw9/dHfHy8Ypg+MzMTixYtgqWlJZo3bw5A/YmUJlWwxt27d7Fnzx5Fe23btgUALFy4UFGe81MkQ0NDdO3aFTt37lTczVzj5Zvp+fv7IzY2VpGuOiUlJddpbq/L398f0dHRijcAGRkZWLx4MWxtbdGkSZNXttG1a1fExsbq7Ft+P/l8WWhoqM5yzZoMTSDZtWtXZGRk4Ouvvy6U474s5w0eNWlV27RpI5dZWFhovUEpiFmzZmHq1KkYNmxYrnVat24NQ0NDLFiwQHFOy5YtQ0pKipzBr1GjRrC1tcWyZcsUb2ZWrVql9cZeM63y5MmTcllGRgZWrVr1yj4HBgYiLS1N553rMzIykJiYmOf+b5JCWrM+Jeff1bx58wBAfi5atWoFAwMDLF26VFFPV2rc9957D4aGhggPD1eUe3p6yumJc0tFW5BrLCEhQb5B6P/93//le7+cLl68CAcHh3ytL6tTpw4qV66MrVu3YuvWrXBzc1PcsyYjI0NrbZCzszMcHR0VU3MTEhJw7do1pKWlvXa/X6ZZX6PJ3vWy7OxsPH78+I3a13zQ8vKIzf379xXZ94pbQEAAqlWrhv/85z/y/xMDAwO4u7sr/g4BaF23gPo+RL/88osiqImNjcXOnTsV9TT34MnPtR8YGIjDhw/La29e9vDhQ/n69vf3R1JSEtavXy9vT09P19nP/AoMDERSUpL8t/uyFy9eyGvidF0LmqyZmmtUs65Ow8jICDVr1lTUISpqHMmhYrV27VocOnRIq3zUqFEYMmQIVqxYgf79++PixYtwd3fHjh07EBYWhvnz58vz5du1a4d58+ahTZs26NWrFx48eIAlS5bA09MTf/75p9ymt7c3evbsiaVLlyIxMRGNGzdGaGiozk/eZs2ahV9//RUNGzbExx9/jHfffRePHz/G77//jqNHj8ov6h9//DEWL16Mjz76CBcvXoSzszM2bdpUqIvIAfUC+9WrV6NXr16KFNLh4eFYtmxZvqZ5adJnjxgxQr4b+7Nnz3D48GFMmDBB540C8+Ln54eaNWuiffv2eOedd+S2Dh48iCZNmsjttW3bFt27d8e3336LyMhI+Y7xJ06cQLt27RSLXwsqMjISXbp0QcuWLXHq1Cls2bIFAwcOlD/5B9QJBA4ePIgFCxbA0dERnp6e8mhTfvj6+r7y0/Ly5ctj/PjxmD17Ntq3bw9/f39cvXoVy5cvR5MmTRAYGAhA/cns9OnTMWrUKPj6+qJ79+64ceMGNm/erDUaV69ePdSpUwfjx4/H/fv3YW1tjc2bN+drFM7Pzw/9+vXD1KlTER4eDl9fXxgaGuLGjRvYvn07Vq1ahfbt2+e6/5ukkG7YsCGCgoKwcOFCPHz4EE2aNMGZM2ewefNmBAUFoVGjRgCAChUqYNiwYViyZAlevHghp5D+9ddfYWtrqxiBs7KyQosWLXD06FGt4GPJkiWIiYnBxx9/jO+//x7+/v6wt7dHQkICTp06hQMHDsDLy0urn5GRkfj+++8hhEBiYiIiIiKwfft2pKamYsmSJXL65tdx9OhRdOzYMd/1NaM5BgYGWolRHj16hCpVqqB79+6oVasWzM3NcfjwYVy+fFkR5M+dOxezZ8/G2bNnC+XGnt7e3pg4cSJmz56Na9euoV27djA3N0dUVBR27NiByZMnv1Hq37Zt22LatGlo06YNBgwYgCdPnmDZsmWoWLFivtfUFDZJkjBu3Dh8/PHH2L59uzyiNnjwYEyaNAl9+vTBBx98gAsXLuDcuXNar/OTJ0/G7t270bx5c4wYMQJGRkZYvnw5qlSpgoiICPma9vT0xKBBg7BmzRo8f/5cTiF99uxZWFhYKK79qVOn4tChQ2jRogUGDRqE2rVrIzExEZcuXcKuXbvw7NkzGBkZoVevXvjuu+8wfPhwREZGwsPDA1u2bMnXtL/cBAQEoEePHpg4cSLOnTsHHx8fSJKEGzduYNu2bdi8eTNatmyJhQsXYseOHejYsSM8PDzw5MkTLF++HPb29vIIVPfu3WFkZISmTZvCxcUFt27dklOIly9f/rX7SFQgxZzNjf6lNCknc/u6d++eEEKI+/fviwEDBohy5coJlUolatWqJdatW6fV3po1a0TlypWFiYmJqFatmli3bp2YOnWqVkrm1NRU8emnn4qyZcsKCwsL0aFDB3Hv3j2tFNKaY48YMUJUqFBBGBsbCycnJ+Hr66tI0SmEENHR0aJjx47C3NxclCtXTowaNUocOnSoUFNICyFEbGys+Oijj0TZsmWFSqUSXl5eivTaQihTIOuSnJwsJk6cKNzd3YWxsbFwcXERgYGBIjo6Os/9daVx3rRpkwgMDBSVKlUSZmZmwszMTNSsWVNMnTpVJCcnK/bPyMgQM2fOFFWqVBEqlUrY29uL9u3bi0uXLina15XPr7HlAAAgAElEQVQmN68U0jdv3hSdO3cWlpaWomzZsmL06NHixYsXiv2vXLkimjRpIkxNTV+ZFvlVz59GzhTSQqjT33733XeiSpUqwtjYWDg7O4uRI0cqUnFrLFiwQLi5uQkTExPRsGFDcebMGdGwYUNFCmkhhLh+/bpo0aKFMDExEc7OzmLq1Kli3759r0whrenP0qVLRZ06dYSpqamwtrYWtWvXFp9//rmIj4/P8/zeJIW0EOrUtVOmTBFubm7C2NhYuLm5iSlTpoj09HRFvYyMDDFp0iTh4OAgzM3NRatWrcTNmzeFhYWFGD16tKLuDz/8IAwNDXX2PSMjQ6xatUo0b95c2NnZCSMjI2Fvby9atWolVq1apbgmNNeT5svAwEDY2tqKevXqiTFjxmilo375+XjVdSGEEH/88YcAIE6fPv3KuhqXL1+W+xMeHq7YlpKSIsaOHStq1aolrKyshKWlpahTp45YtWqVop7mb0JXOujc5JVCWuP7778X77//vjA3NxdWVlbi3XffFaNHjxZ3796V63h5eYkmTZro3N/Gxkb07t1b57YtW7aI6tWrCxMTE+Hp6SkWL14sp3fWpFvWtK8rhfTLKe2F+N9z/3K6ZF00x9CV8jgtLU04OTkJb29vuSw9PV2MHDlS2NnZCQsLCxEQECDu3bunlUJaCCH/LZuYmAg3NzexYMEC8dVXXwkAIi0tTa734sULMX78eGFvby/Mzc1F27ZtRVRUlDA2NhaTJk1StPn48WMxduxY+XXbwcFBNGvWTCxYsECRkjk+Pl50795dWFhYCDs7OzFkyBBx+vRpnSmky5cvr3Xuo0aNEjY2NoqyrKwssWDBAuHl5SVMTU2FjY2N8Pb2Fl988YVISEiQz7lbt27C1dVVqFQq4eTkJLp06SIuX74st7Nx40bx4YcfCnt7e6FSqYS7u7v49NNPtW7TQFSUJCHecP4IEVERmzRpEmbPno1nz55ppQ+m0i0+Ph7Ozs6YM2eOnHYaUE+9qVq1KgYPHowvvviiBHuYt6FDh+LSpUs6pxfRv1P//v2xf/9+xVRnXTTr2BYvXqyVHp+I3hzX5BARUbHImTYY+N9anpyZ7FQqFaZNm4aFCxfq3O9tEB8fjw0bNuhcf0b/DjmvzX/++Qc7duzQup4Lcu0TUeHgmhwiIioWGzduxPbt29GmTRuYm5vj+PHj2L59Ozp27ChnpHtZv379ck3r/jZwcnJ6awMwKh61a9dGp06dULlyZfzzzz9YsWIFMjMztdaSrVy5EgcOHJBv9Hns2DHs3r0bgYGBqFGjRgn1nki/McghIqJi4e3tja1bt2LWrFlISkqCs7Mzxo8fj+nTp5d014heS9u2bbFr1y7ExsbCyMgI7733HkJCQlCnTh1Fvfr162P37t345ptv8OzZM5QvXx6TJk3Smc2OiAoH1+QQEREREZFe4ZocIiIiIiLSKwxyiIiIiIhIrzDIISIiIiIivcIgh4iIiIiI9AqDHCIiIiIi0isMcoiIiIiISK8wyCEiIiIiIr3CIIeIiIiIiPQKgxwiIiIiItIrDHKIiIiIiEivMMghIiIiIiK9wiCHiIiIiIj0CoMcIiIiIiLSKwxyiIiIiIhIrzDIISIiIiIivcIgh4iIiIiI9AqDHCIiIiIi0isMcoiIiIiISK8wyCEiIiIiIr3CIIeIiIiIiPQKgxwiIiIiItIrDHKIiIiIiEivMMghIiIiIiK9wiCHiIiIiIj0CoMcIiIiIiLSKwxyiIiIiIhIrzDIISIiIiIivcIgh4iIiIiI9AqDHCIiIiIi0isMcoiIiIiISK8wyCEiIiIiIr3CIIeIiIiIiPQKgxwiIiIiItIrDHKIiIiIiEivMMghIiIiIiK9wiCHiIiIiIj0CoMcIiIiIiLSKwxyiIiIiIhIrzDIISIiIiIivWJU0h3IKTs7G7GxsbCysoIkSSXdHSIiIiIiKiFCCDx79gwuLi4wMMj/+MxbF+TExsaiQoUKJd0NIiIiIiJ6S9y7dw+urq75rv/WBTlWVlYA1CdibW1dwr0hIiIiIqKSkpSUhAoVKsgxQn69dUGOZoqatbU1gxwiIiIiIirwMhYmHiAiIiIiIr3CIIeIiIiIiPQKgxwiIiIiItIrb92aHCIiIiJSysrKQkZGRkl3g6hIqFSqAqWHzg8GOURERERvKSEE4uPj8fTp05LuClGRMTAwgIeHB1QqVaG1ySCHiIiI6C2lCXAcHBxgbm7OG6WT3snOzkZsbCzi4uJQsWLFQrvGGeQQERERvYWysrLkAKds2bIl3R2iImNvb4/Y2FhkZmbC2Ni4UNpk4gEiIiKit5BmDY65uXkJ94SoaGmmqWVlZRVamwxyiIiIiN5inKJG+q4ornEGOUREREREpFcY5BARERFRiQgLC0OtWrVgbGyMTp065Vpv2rRp8Pb2LsaeFa/169fD1ta2pLuhVxjkEBEREVGROHv2LAwNDdGuXTud28eOHQtvb2/cuXMH69evz7Wd8ePHIzQ0tIh6Wbzc3d0xf/58RVlQUBBu3LhRQj3STwxyiIiIiKhIrFmzBiNHjsTJkycRGxurtT0qKgoffvghXF1ddY5kCCGQmZkJS0tLvc4wZ2ZmBgcHh5Luhl5hkENEREREhS45ORlbt27FsGHD0K5dO8VIzd27dyFJEh49eoSBAwdCkiSsX78ex48fhyRJOHjwIOrVqwcTExOcPn1a53S1tWvXokaNGjAxMYGzszOCg4PlbfPmzUOtWrVgYWGBChUqYPjw4UhOTpa3a6aHHT58GNWrV4elpSXatGmDuLi4XM9H07fQ0FDUr18f5ubmaNy4Ma5fvy7XiYqKQkBAABwdHWFpaYkGDRrg6NGj8nYfHx9ER0djzJgxkCRJXnCva7rasmXLUKlSJahUKlStWhWbNm1SbJckCatXr0bnzp1hbm6OypUrY+/evfL2J0+eoHfv3rC3t4eZmRkqV66MdevW5fUr0ysMcoiIiIhKCSEEUtIzS+RLCFGgvm7btg3VqlVD1apV0adPH6xdu1Zuo0KFCoiLi4O1tTXmz5+PuLg4BAUFyftOmjQJs2bNQmRkJGrXrq3V9rJlyzBixAgMGTIEly9fxt69e+Hp6SlvNzAwwMKFC/HXX39hw4YNOHbsGCZMmKBoIyUlBXPmzMGmTZtw8uRJxMTEYPz48a88ry+++AJz585FeHg4jIyMMHDgQHlbcnIy/P39ERoaij/++ANt2rRBhw4dEBMTAwDYtWsXXF1dERISgri4uFyDqt27d2PUqFEYN24crly5gk8++QQDBgzAr7/+qqg3ffp0BAYG4s8//4S/vz969+6Nx48fAwCmTJmCq1ev4uDBg4iMjMSyZctQrly5V56fvuDNQImIiIhKidSMLLz75eESOfbVED+Yq/L/1nHNmjXo06cPAKBNmzZITEzEiRMn4OPjA0NDQzg5OUGSJNjY2MDJyUmxb0hICFq1apVr2zNmzMC4ceMwatQouaxBgwby96NHj5a/d3d3x4wZMzB06FAsXbpULs/IyMDy5ctRqVIlAEBwcDBCQkJeeV5ff/01mjdvDkAdjLVr1w5paWkwNTWFl5cXvLy85LpfffUVdu/ejb179yI4OBhlypSBoaEhrKystM75ZXPmzEH//v0xfPhwAOq1S+fOncOcOXPQokULuV7//v3Rs2dPAMA333yDhQsX4sKFC2jTpg1iYmJQp04d1K9fX34e/k04kkNEREREher69eu4cOGC/AbcyMgIQUFBWLNmTb7217wx1+XBgweIjY2Fr69vrnWOHj0KX19flC9fHlZWVujbty8ePXqElJQUuY65ubkc4ACAs7MzHjx48Mq+vTyy5OzsLPcJUI/kjB8/HtWrV4etrS0sLS0RGRkpj+TkV2RkJJo0aaIoa9KkCSIjI3Pti4WFBaytreW+DBs2DFu2bIG3tzcmTJiAM2fOFKgPpR1HcoiIiIhKCTNjQ1wN8SuxY+fXmjVrkJmZCRcXF7lMCAETExMsXrwYNjY2ee5vYWGRez/MzPLc9+7du2jfvj2GDRuGr7/+GmXKlMHp06cxaNAgpKenw9zcHABgbGys2E+SpHxNyXt5P82amuzsbADqLHBHjhzBnDlz4OnpCTMzM3Tr1g3p6emvbPd16DoHTV/atm2L6OhoHDhwAEeOHIGvry9GjBiBOXPmFElf3jYMcoiIiIhKCUmSCjRlrCRkZmZi48aNmDt3Llq3bq3Y1qlTJ/z4448YOnToa7dvZWUFd3d3hIaGKqZuaVy8eBHZ2dmYO3cuDAzUk5a2bdv22scriLCwMPTv3x+dO3cGoB7ZuXv3rqKOSqVCVlZWnu1Ur14dYWFh6Nevn6Ltd999t0D9sbe3R79+/dCvXz80bdoUn332GYMcIr0QnkcWkfoDiq8fRERE/xL79u3DkydPMGjQIK0Rm65du2LNmjVvFOQA6puDDh06FA4ODmjbti2ePXuGsLAwjBw5Ep6ensjIyMCiRYvQoUMHhIWFYfny5W90vPyqXLkydu3ahQ4dOkCSJEyZMkUeWdFwd3fHyZMn0aNHD5iYmOhMBvDZZ58hMDAQderUQcuWLfHzzz9j165dikxtr/Lll1+iXr16qFGjBl68eIF9+/ahevXqb3yOpQXX5BARERFRoVmzZg1atmypc0pa165dER4ejj///PONjtGvXz/Mnz8fS5cuRY0aNdC+fXvcvHkTAODl5YV58+Zh9uzZqFmzJjZv3oyZM2e+0fHya968ebCzs0Pjxo3RoUMH+Pn5oW7duoo6ISEhuHv3LipVqgR7e3ud7XTq1AkLFizAnDlzUKNGDaxYsQLr1q2Dj49PvvuiUqnw+eefo3bt2mjWrBkMDQ2xZcuWNzm9UkUSBc0HWMSSkpJgY2ODxMREWFtbl3R3qLTjSA4REZVSaWlpuHPnDjw8PGBqalrS3SEqMnld668bG3Akh4iIiIiI9AqDHCIiIiIi0isMcoiIiIiISK8wyCEiIiIiIr3CIIeIiIiIiPQKgxwiIiIiItIrDHKIiIiIiEivMMghIiIiIiK9wiCHiIiIiIj0CoMcIiIiIvpXEkJgyJAhKFOmDCRJQkRERK51JUnCnj17irF3xcvd3R3z588v6W4UGgY5RERERFSo+vfvD0mSMGvWLEX5nj17IElSsfbFz88PhoaG+O2337S2HTp0COvXr8e+ffsQFxeHmjVr5tpOXFwc2rZtW5RdLRbr16+Hra2tVvlvv/2GIUOGlECPikaBg5yTJ0+iQ4cOcHFx0YpoMzIyMHHiRNSqVQsWFhZwcXHBRx99hNjY2ELtNBERERG93UxNTTF79mw8efKkxPoQExODM2fOIDg4GGvXrtXaHhUVBWdnZzRu3BhOTk4wMjLSqpOeng4AcHJygomJSZH3uaTY29vD3Ny8pLtRaAoc5Dx//hxeXl5YsmSJ1raUlBT8/vvvmDJlCn7//Xfs2rUL169fR8eOHQuls0RERERUOrRs2RJOTk6YOXNmnvV27tyJGjVqwMTEBO7u7pg7d65iu7u7O7755hsMHDgQVlZWqFixIlauXJmvPqxbtw7t27fHsGHD8OOPPyI1NVXe1r9/f4wcORIxMTGQJAnu7u4AAB8fHwQHB2P06NEoV64c/Pz8AGhPV/v777/Rs2dPlClTBhYWFqhfvz7Onz8PQB08BQQEwNHREZaWlmjQoAGOHj36xufl4+ODTz/9FBMmTECZMmXg5OSEadOmKerMmzdPHnCoUKEChg8fjuTkZADA8ePHMWDAACQmJkKSJEiSJO+fc7paTEwMAgICYGlpCWtrawQGBuL+/fvy9mnTpsHb2xubNm2Cu7s7bGxs0KNHDzx79kyus2PHDtSqVQtmZmYoW7YsWrZsiefPn+d5joWlwEFO27ZtMWPGDHTu3Flrm42NDY4cOYLAwEBUrVoV77//PhYvXoyLFy8iJiZGZ3svXrxAUlKS4ouIiIiIdBACSH9eMl9CFKirhoaG+Oabb7Bo0SL8/fffOutcvHgRgYGB6NGjBy5fvoxp06ZhypQpWL9+vaLe3LlzUb9+ffzxxx8YPnw4hg0bhuvXr7/iqRJYt24d+vTpg2rVqsHT0xM7duyQty9YsAAhISFwdXVFXFycYjrbhg0boFKpEBYWhuXLl2u1nZycjObNm+Off/7B3r17cenSJUyYMAHZ2dnydn9/f4SGhuKPP/5AmzZt0KFDB633w69zXhs2bICFhQXOnz+Pb7/9FiEhIThy5Ii83cDAAAsXLsRff/2FDRs24NixY5gwYQIAoHHjxpg/fz6sra0RFxeHuLg4jB8/XusY2dnZCAgIwOPHj3HixAkcOXIEt2/fRlBQkKJeVFQU9uzZg3379mHfvn04ceKEPEUxLi4OPXv2xMCBAxEZGYnjx4+jS5cuEAW8jl6X9phcIdNEirrm/gHAzJkzMX369KLuBhEREVHpl5ECfONSMsf+v1hAZVGgXTp37gxvb29MnToVa9as0do+b948+Pr6YsqUKQCAKlWq4OrVq/jPf/6D/v37y/X8/f0xfPhwAMDEiRPx3Xff4ddff0XVqlVzPfbRo0eRkpIij8T06dMHa9asQd++fQGoP5y3srKCoaEhnJycFPtWrlwZ3377ba5t//DDD0hISMBvv/2GMmXKAAA8PT3l7V5eXvDy8pJ//uqrr7B7927s3bsXwcHBb3RetWvXxtSpU+V+Ll68GKGhoWjVqhUAYPTo0XJdd3d3zJgxA0OHDsXSpUuhUqlgY2MDSZK0zvlloaGhuHz5Mu7cuYMKFSoAADZu3IgaNWrgt99+Q4MGDQCog6H169fDysoKANC3b1+Ehobi66+/RlxcHDIzM9GlSxe4ubkBAGrVqpXrMQtbkSYeSEtLw8SJE9GzZ09YW1vrrPP5558jMTFR/rp3715RdomIiIiIitHs2bOxYcMGREZGam2LjIxEkyZNFGVNmjTBzZs3kZWVJZfVrl1b/l7zBv3BgwcA1LOMLC0tYWlpiRo1asj11q5di6CgIHmdTc+ePREWFoaoqKhX9rlevXp5bo+IiECdOnXkACen5ORkjB8/HtWrV4etrS0sLS0RGRmpNZKT13nl5uV9AMDZ2Vmxz9GjR+Hr64vy5cvDysoKffv2xaNHj5CSkpJnuy+LjIxEhQoV5AAHAN59913Y2toqfo/u7u5ygJOzL15eXvD19UWtWrXQvXt3rFq1qljXZxXZSE5GRgYCAwMhhMCyZctyrWdiYqLXi7iIiIiICo2xuXpEpaSO/RqaNWsGPz8/fP7554rRmQId2thY8bMkSfLUsNWrV8trbTT1Hj9+jN27dyMjI0PxPjQrKwtr167F119/nefxLCzyHrEyMzPLc/v48eNx5MgRzJkzB56enjAzM0O3bt3kJAb5Oa/c5LXP3bt35TVIX3/9NcqUKYPTp09j0KBBSE9PL/TEAnn1xdDQEEeOHMGZM2fwyy+/YNGiRfjiiy9w/vx5eHh4FGo/dCmSIEcT4ERHR+PYsWO5juIQERERUQFIUoGnjL0NZs2aBW9vb61pWNWrV0dYWJiiLCwsDFWqVIGhoWG+2i5fvrxW2ebNm+Hq6qp1X5tffvkFc+fORUhISL7b16V27dpYvXo1Hj9+rHM0JywsDP3795fXsCcnJ+Pu3buvfbz8unjxIrKzszF37lwYGKgnbG3btk1RR6VSKUbJdKlevTru3buHe/fuyaM5V69exdOnT/Huu+/muz+SJKFJkyZo0qQJvvzyS7i5uWH37t0YO3ZsAc+s4Ap9upomwLl58yaOHj2KsmXLFvYhiIiIiKgUqVWrFnr37o2FCxcqyseNG4fQ0FB89dVXuHHjBjZs2IDFixfrXAxfEGvWrEG3bt1Qs2ZNxdegQYPw8OFDHDp06I3a79mzJ5ycnNCpUyeEhYXh9u3b2LlzJ86ePQtAvVZm165diIiIwKVLl9CrV69XjtAUBk9PT2RkZGDRokW4ffs2Nm3apJU4wd3dHcnJyQgNDcXDhw91TmNr2bKl/Dv7/fffceHCBXz00Udo3rw56tevn6++nD9/Ht988w3Cw8MRExODXbt2ISEhAdWrVy+Uc32VAgc5ycnJiIiIkO8Ie+fOHURERCAmJgYZGRno1q0bwsPDsXnzZmRlZSE+Ph7x8fFaw3NERERE9O8REhKi9Ua/bt262LZtG7Zs2YKaNWviyy+/REhIyGtPawPUoxmXLl1C165dtbbZ2NjA19dXZxKEglCpVPjll1/g4OAAf39/1KpVC7NmzZJHh+bNmwc7Ozs0btwYHTp0gJ+fH+rWrftGx8wPLy8vzJs3D7Nnz0bNmjWxefNmrRTejRs3xtChQxEUFAR7e3udCRYkScJPP/0EOzs7NGvWDC1btsQ777yDrVu35rsv1tbWOHnyJPz9/VGlShVMnjwZc+fOLbYbqkqigHncjh8/jhYtWmiV9+vXD9OmTct1jt2vv/4KHx+fV7aflJQEGxsbJCYmcpobvbnwdblvqz+g+PpBRERUQGlpabhz5w48PDxgampa0t0hKjJ5XeuvGxsUeE2Oj49Pnvmtiyv3NRERERERkS5FmkKaiIiIiIiouDHIISIiIiIivcIgh4iIiIiI9AqDHCIiIiIi0isMcoiIiIjeYsVxfxWiklQUicsKnF2NiIiIiIqeSqWCgYEBYmNjYW9vD5VKBUmSSrpbRIVKCIGEhARIkgRjY+NCa5dBDhEREdFbyMDAAB4eHoiLi0NsbGxJd4eoyEiSBFdXV/lmqoWBQQ4RERHRW0qlUqFixYrIzMxEVlZWSXeHqEgYGxsXaoADMMghIiIieqtppvEU5lQeIn3HxANERERERKRXGOQQEREREZFeYZBDRERERER6hUEOERERERHpFQY5RERERESkVxjkEBERERGRXmGQQ0REREREeoVBDhERERER6RUGOUREREREpFcY5BARERERkV5hkENERERERHqFQQ4REREREekVBjlERERERKRXGOQQEREREZFeYZBDRERERER6hUEOERERERHpFQY5RERERESkVxjkEBERERGRXmGQQ0REREREeoVBDhERERER6RUGOUREREREpFcY5BARERERkV5hkENERERERHqFQQ4REREREekVo5LuAFGRS7gOxF0CXjwDsl4AlXwB+6ol3SsiIiIiKiIMcki/ZaQC4WvVwY1GymOgxf+VXJ+IiIiIqEhxuhrpt3vn1QGOeTmgZjfA2BxIeQjE/VnSPSMiIiKiIsIgh/RXdhZw95T6+0otAPcPAPem6p+jQgEhSq5vRERERFRkGOSQ/rpxCEh5pB69cW2gLvNoChgYA4n3gNvHS7R7RERERFQ0GOSQ/jq3TP1Y8X3AUKX+XmUJVGyk/j5sfsn0i4iIiIiKFIMc0k/3r6qnqkkG/5uipvGOj7r89nHg8Z0S6BwRERERFSUGOaSfru9XPzpUB8zslNvMywC2burvo8OKt19EREREVOQY5JB+ij6rfixXRff2spXUj3cZ5BARERHpGwY5pH+ys4B7F9Tfl6mku46mPPp08fSJiIiIiIoNgxzSP/GXgfRngIk1YO2iu04ZD0AyBJ7GAE/vFW//iIiIiKhIMcgh/RNzTv1Y4T11ggFdjEwBZy/199FniqdfRERERFQsGOSQ/on5b9CiSRWdG/cm6kdOWSMiIiLSKwxySL8I8b+kA26N867r9oH6kSM5RERERHqFQQ7pl8e3gecP1Df/dKmbd92K7wOQgEe3gGfxxdI9IiIiIip6DHJIv2hGZVzqAsamedc1swWcav53P6aSJiIiItIXDHJIv8Ropqq9Yj2OhmbKmiZZARERERGVegxySL9o7o/zqqQDGi511I/xV4qmP0RERERU7BjkkP5IT1GvrwEAZ+/87eNYQ/14/y910gIiIiIiKvUY5JD+SIgEIADzcoClQ/72KVcFMDACXiQCiX8XafeIiIiIqHgwyCH9cf+q+tGxBiBJ+dvHSAWUq/rf/f8qmn4RERERUbFikEP6QxOkONYs2H7ylDWuyyEiIiLSBwxySH9oghRN0JJfL6/LISIiIqJSj0EO6QchXhrJebdg+2pGfhjkEBEREekFo5LuANEbCV+nfkxLBFIfA5DUaaRjI/LfhmYk59FNICPt1TcRJSIiIqK3GkdySD8kxaofLe0BQ1XB9rVyAszKACIbSLhW+H0jIiIiomJV4CDn5MmT6NChA1xcXCBJEvbs2aPYLoTAl19+CWdnZ5iZmaFly5a4efNmoXWYSKdncepHK5eC7ytJXJdDREREpEcKHOQ8f/4cXl5eWLJkic7t3377LRYuXIjly5fj/PnzsLCwgJ+fH9LS0t64s0S50ozkWL9GkAMATrXUjwxyiIiIiEq9Aq/Jadu2Ldq2batzmxAC8+fPx+TJkxEQEAAA2LhxIxwdHbFnzx706NHjzXpLlJtn/w1yXmckB2AaaSIiIiI9Uqhrcu7cuYP4+Hi0bNlSLrOxsUHDhg1x9uxZnfu8ePECSUlJii+iAsnOAp7dV39v7fx6bbwc5AhROP0iIiIiohJRqEFOfHw8AMDR0VFR7ujoKG/LaebMmbCxsZG/KlSoUJhdon+D5AeAyAKMTNQJBF6HfTUAEpDyCHieUKjdIyIiIqLiVeLZ1T7//HMkJibKX/fu3SvpLlFpk6xJOuCsTiLwOozNANuK6u8fMlEGERERUWlWqEGOk5MTAOD+/fuK8vv378vbcjIxMYG1tbXii6hAkh+oHy0d8673KuUqqx8fMcghIiIiKs0KNcjx8PCAk5MTQkND5bKkpCScP38ejRo1KsxDEf1P8n+D6jcNcsr+N8jhSA4RERFRqVbg7GrJycm4deuW/POdO3cQERGBMmXKoGLFihg9ejRmzJiBypUrw8PDA1OmTIGLiws6depUqB0nkskjOQ5v1k45BjlERERE+qDAQU54eDhatGgh/xB6b3sAACAASURBVDx27FgAQL9+/bB+/XpMmDABz58/x5AhQ/D06VN88MEHOHToEExNTQuv10QaIvt/iQIsOF2NiIiIiF4jyPHx8YHII8WuJEkICQlBSEjIG3WMKF/SEoGsdEAyBMxfM7Oahma62pO7QOYLdbY2IiIiIip1ChzkEL1VNOtxLMoBBoYF2zd8nfJnIQBDEyDrBfD4DuBQrXD6SERERETFqsRTSBO9kcJKOgCo009r1vVwyhoRERFRqcUgh0q35P+ux3nTpAMamnaYfICIiIio1GKQQ6WbPJJTSEGOBYMcIiIiotKOQQ6Vbpr00RaFNZLz32lvnK5GREREVGoxyKHSKy0JeJGo/r4opqvlkUWQiIiIiN5eDHKo9NKMtphYA8bmhdOmRTkAEpD2FHj+sHDaJCIiIqJixSCHSq+Ht9SPhTWKAwCGKsDMVv09p6wRERERlUoMcqj0enhD/VhY63E0NOtymHyAiIiIqFRikEOllybIKcyRHOB/QRNHcoiIiIhKJQY5VHo9KoLpagBgYa9+fHyncNslIiIiomLBIIdKp+xs4PFt9feFPV3Nopz68VFU4bZLRERERMWCQQ6VTs9igcw0QDIAzOwKt23NSM6TO+pgioiIiIhKFQY5VDppRlnMywIGhoXbtpkdYGCkDqKexRZu20RERERU5BjkUOmkWY+jGXUpTAaGgK2b+nvNlDgiIiIiKjUY5FDpJK/HKYIgBwDKvKN+5LocIiIiolKHQQ6VTprgo6iCnLKV1I8cySEiIiIqdYxKugNErxS+Trss9qL6sahHchjkEBEREZU6HMmh0ic7C3j+SP19kQU5HMkhIiIiKq0Y5FDpk/oEEFnqDGhmtkVzjDIe6sfHTCNNREREVNowyKHS53mC+tG8nPo+OUXB1u2/aaRTgWdxRXMMIiIiIioSDHKo9Hn+UP1YVFPVAMDQCLCtqP6eU9aIiIiIShUGOVT6aEZyijLIAV5al8M00kRERESlCYMcKn2eP1A/FnmQwwxrRERERKURgxwqfeTpauWK9jiae+XwhqBEREREpQqDHCpdsrOA1Mfq74ttJOdO0R6HiIiI/p+9+46Ou7rzPv6ZIo16lyzJKpZ7xR0DNgYSA6FDSAjBEAKBFEwIPJtswu4mz+5mE4fNPiwJENMWUw0kmxBKaAnNxg33Am6yrOIiq/c65fnjp5HtYOM2M3fK+3WOzm+syJ7PyeHY+uje+71AQFFyEFm6GiWfV3LESwnpwX2vw7er+XzBfS8AAAAEDCUHkeWI8dG24L5XRolkcwyMka4N7nsBAAAgYCg5iCyhGB/t54hjjDQAAEAEouQgsnSFaOiAX1aZ9aTkAAAARAxKDiJLqO7I8WOMNAAAQMSh5CCyhGp8tJ+/5DQzYQ0AACBSUHIQOQ4fH50U4pLDSg4AAEDEoOQgcnQ3WeOj7XFSQlpo3jPTfyZnD2OkAQAAIgQlB5Hj8K1qthD9p5s5TJJN6m2z7ugBAABA2KPkIHKEeuiAJMUlSGlDrddNnMsBAACIBJQcRA7/Sk6ozuP4MUYaAAAgolByEDlCfUeOHyUHAAAgolByEDlMbFeTmLAGAAAQYSg5iAxez6GD/yFfyeGuHAAAgEhCyUFk6G4eGB/tlBLSQ/vemWxXAwAAiCSUHESGrsOGDoRqfLSf/0xOV6PU0xra9wYAAMBJo+QgMnQaGjogSa5UKTnPes0YaQAAgLBHyUFkMDV0wI/hAwAAABGDkoPIMHhHTraZ9x8cI73bzPsDAADghFFyEBm6wmUlp9LM+wMAAOCEUXIQ/nxec+Oj/diuBgAAEDEoOQh/Pa3WPTk2h5SYaSYDJQcAACBiUHIQ/vxDB5KyQj8+2s9/JqejVurtMJMBAAAAJ4SSg/A3OD7a0HkcyVpBSsyyXjczRhoAACCcUXIQ/joPuwjUJLasAQAARARKDsJfl38lx9D4aD9KDgAAQERwmg4AHNfgSk4It6utXfzZz/W2Wc8db0lz7gldFgAAAJwUVnIQ3ny+w1ZyDG9X82+X8+cBAABAWKLkILx1HJQ8fZJs1nQ1k/wlyz/tDQAAAGGJkoPw5j//kpgp2Q3vrvRPd+tplfq6zGYBAADAMVFyEN6aBsY1m96qJklxSVJcovW6udJoFAAAABwbJQfhzb+SEw4lx2Y7dC6HCWsAAABhi5KD8OYvE6GcrPZ5kik5AAAA4Y6Sg/A2uJJj+I4cP/+5nKbdZnMAAADgmAJecjwej37605+qrKxMiYmJGjFihH7+85/L5/MF+q0Q7Xy+Q2dyWMkBAADACQr4uKr77rtPixYt0tNPP60JEyZo7dq1uuWWW5Senq677ror0G+HaNbdLPW2Wq/DZSXHX7YaKTkAAADhKuAlZ8WKFbrqqqt02WWXSZKGDRumF154QR9//PFRv763t1e9vb2Dv25rawt0JEQq/2qJK11yxJvN4udfyWnbJ/X3SHEJZvMAAADgMwK+Xe2cc87Ru+++q507d0qSNm3apI8++kiXXHLJUb9+4cKFSk9PH/woLi4OdCREqnAaH+0XnyI5EyT5pOY9ptMAAADgKAJecn7yk5/o+uuv19ixYxUXF6epU6fq7rvv1vz584/69ffee69aW1sHP2pqagIdCZEq3IYOSNYYaf/wgUaGDwAAAISjgG9X+/3vf6/nn39eS5Ys0YQJE7Rx40bdfffdKiws1M033/yZr3e5XHK5XIGOgWjgXylJCqOVHMlaWWqtYcIaAABAmAp4yfnRj340uJojSZMmTVJVVZUWLlx41JIDHFNTuJYcVnIAAADCWcC3q3V1dcluP/KPdTgc8nq9gX4rRLvB7WrhVnLyrCclBwAAICwFfCXniiuu0C9+8QuVlJRowoQJ2rBhg+6//37deuutgX4rRLPeDqmzznoddis5/rtyKDkAAADhKOAl58EHH9RPf/pT3XHHHaqrq1NhYaG+853v6Gc/+1mg3wrRrLnSeiZkSPFJRqN8hn+7WvsBqa9Tik82mwcAAABHCHjJSU1N1QMPPKAHHngg0H80Yol/6EDWcLM5jiY+WUrMtC4rbaqQ8ieZTgQAAIDDBPxMDhAQ/vM4WWVmcxxL1gjrybkcAACAsEPJQXjyT1bLDNOSkz1QcjiXAwAAEHYoOQhPg9vVwrTkDK7kVJjNAQAAgM+g5CA8NYXxmRyJlRwAAIAwRslB+HH3Sa011utw3a7mL1+cyQEAAAg7lByEn9YayeeVnIlSar7pNEfnX8nprJN62sxmAQAAwBEoOQg/g0MHhkk2m9Eox5SQfuiSUrasAQAAhBVKDsJPON+Rc7jskdaTLWsAAABhhZKD8BPud+T4DQ4fYMIaAABAOKHkIPwcvl0tnPlLTmO52RwAAAA4AiUH4ae50nqG/UoO29UAAADCkdN0AOAIPt+hkhOu46Mlae1iqW2/9bruE2nNk4eGJMy4xVwuAAAAsJKDMNNeK7m7JZtdSi82nebzJQ9MV+vvlvo7zWYBAADAIEoOwot/slp6keSMN5vleBzxUkKG9bqj3mwWAAAADKLkILxEwla1w6XkWc/OOrM5AAAAMIiSg/ASKZPV/JJzrWcnKzkAAADhgpKD8DJ4EWiErORQcgAAAMIOJQfhJdK2qyUPbFfjTA4AAEDYoOQgvDRF8EqOz2s2CwAAACRRchBOetulrgbrdaScyUnKssZde/ulnlbTaQAAACAuA0U4WLvYerbutZ5xydLWP5nLczLsDikp21rJ6ayXEjNNJwIAAIh5rOQgfHQ1Ws/kbLM5TtbguRzGSAMAAIQDSg7Ch7/kJOWYzXGyUpiwBgAAEE4oOQgfnQPncZIibSWHkgMAABBOKDkIH4Pb1SJsJcdfctiuBgAAEBYoOQgf/slqkbZdzX8mp7tJ8nrMZgEAAAAlB2HC65G6m63XkbZdLSFNcsRb9+R0NZlOAwAAEPMoOQgP3c1WSbA7rdIQSWz2Q6tPnWxZAwAAMI2Sg/AwOFkt2yoNkYYJawAAAGEjAr+bRFTqitDJan5MWAMAAAgblByEh8NXciIRJQcAACBsUHIQHiL1IlA//4Q1Sg4AAIBxlByEh07/HTkRvpLT3Sz1d5vNAgAAEOMoOTDP54v8MznxyVJcovW6qcJsFgAAgBhHyYF5/V2Su8d6Haklx2Y7tJrTuNtsFgAAgBhHyYF5/vM4roFLNSPVYMkpN5sDAAAgxlFyYF6kb1XzYyUHAAAgLFByYJ5/JSc5Qier+fknrDVRcgAAAEyi5MC8zgi/I8eP7WoAAABhgZID8yL9IlC/wy8E7Wk1mwUAACCGUXJgXrScyYlLsIYnSJzLAQAAMIiSA7PcfVJ3i/U6KcLP5EiHzhVRcgAAAIyh5MCs1hpJPskeJ7lSTac5ff4ta1wICgAAYAwlB2Y177GeydnWhZqRzr+SQ8kBAAAwhpIDs5oGSk40bFWTpCRWcgAAAEyj5MCs5krrGelDB/wGV3I4kwMAAGAKJQdmRWvJ6Wo8NFABAAAAIUXJgVmDJSdKtqs5E6TkPOu1/7wRAAAAQoqSA3N8vkMlJzlKVnIkKXuE9eRcDgAAgBGUHJjT1Sj1dUiySYlZptMETtZw69lIyQEAADDBaToAYph/slpCuuSIM5slkLqbreeutw+d0TncjFtCmwcAACDGsJIDc6Jt6ICf/0LQzgazOQAAAGIUJQfmRG3JGVi96aw3mwMAACBGUXJgjn/62NG2dEUy/6S4vg6pv8dsFgAAgBhEyYE50bqSE5coxadYr7vYsgYAABBqlByYE60lR+JcDgAAgEGUHJjR3yO17bdeR8tFoIfjXA4AAIAxlByY0VItyWdt64pPNp0m8PzFrYuSAwAAEGqUHJjhHzqQWSbZbGazBAPb1QAAAIwJSsnZt2+fbrzxRmVnZysxMVGTJk3S2rVrg/FWiFT+8ziZpUZjBM3gdrVGszkAAABikDPQf2Bzc7Nmz56tCy64QG+++aZyc3O1a9cuZWZmBvqtEMkGS84wkymCxz9MobdV8vRJjnizeQAAAGJIwEvOfffdp+LiYi1evHjwc2VlZYF+G0S6poHtallR+t9GXJLkTJDcPVJXk5SabzoRAABAzAj4drVXX31VM2bM0Fe/+lXl5eVp6tSpevzxx4/59b29vWprazviAzEg2ldybLbDhg+wZQ0AACCUAl5yKioqtGjRIo0aNUpvv/22vve97+muu+7S008/fdSvX7hwodLT0wc/iouLAx0J4cbnO6zkROlKjnRoyxoXggIAAIRUwEuO1+vVtGnT9Mtf/lJTp07Vt7/9bd1+++165JFHjvr19957r1pbWwc/ampqAh0J4abjoOTulmx2KT2KS+1gyWElBwAAIJQCXnIKCgo0fvz4Iz43btw4VVdXH/XrXS6X0tLSjvhAlPOfx0krkpxRfCA/mZIDAABgQsBLzuzZs7Vjx44jPrdz506VlkbpqGCcPP8dOVnDjMYIuiTGSAMAAJgQ8JJzzz33aNWqVfrlL3+p8vJyLVmyRI899pgWLFgQ6LdCpGo67CLQaHb4djWfz2wWAACAGBLwkjNz5ky9/PLLeuGFFzRx4kT9/Oc/1wMPPKD58+cH+q0QqZqjfHy0X2KmJJvk7Zd6mRoIAAAQKgG/J0eSLr/8cl1++eXB+KMRDWJlJcfusIpOd5O1mpOQbjoRAABATAj4Sg5wXLGykiMxRhoAAMAASg5Cq6ft0LSxaF/JkaRkhg8AAACEGiUHoeVfxUnKlhJiYFw4d+UAAACEHCUHoRUr53H8KDkAAAAhR8lBaMXSeRyJkgMAAGAAJQehFasrOb1tkqfPbBYAAIAYQclBaMXaSk58shSXaL1mNQcAACAkKDkIraZK6xkrKzmSlMiWNQAAgFCi5CB03H1S217rdays5EhSUpb17GoymwMAACBGUHIQOi3Vks8rxSVJKUNMpwkdf8nppuQAAACEAiUHoeM/j5M5TLLZjEYJqcHtapQcAACAUKDkIHRibbKaH9vVAAAAQoqSg9CJtclqfolsVwMAAAglSg5Cp6nCemYNN5sj1PwrOf1dUn+32SwAAAAxgJKD0InVkuN0WfflSKzmAAAAhAAlB6Hh9UjNldbrWCs50qEta5zLAQAACDpKDkKjda/k6ZMc8VJ6kek0oTc4fIALQQEAAIKNkoPQ8G9Vyxwm2R1GoxjhHyPNdjUAAICgo+QgNGL1PI4fY6QBAABCxmk6AGLEttesZ3+3tHax2SwmcCYHAAAgZFjJQWh01lvP5ByzOUxJOuyuHJ/PbBYAAIAoR8lBaHQ1WM/kXLM5TPGXHHeP1NNiNgsAAECUo+Qg+LxeqXNgqlhSjK7kOOKl+BTrdXOV2SwAAABRjpKD4GvfL3n7JZtdSsw0ncYc/2pOS7XZHAAAAFGOkoPg809WS8qOzfHRfkkDY6QpOQAAAEFFyUHw+UtOrJ7H8UtkJQcAACAUKDkIvsGVnBg9j+M3uF2NMzkAAADBRMlB8A2u5MR4yWElBwAAICQoOQi+RkqOpENDF1pquCsHAAAgiCg5CC6fT2reY71OivUzOQMlp6+du3IAAACCyGk6AKJc+wGpv8saH+0/kxKrnC4pPlnq67RWc2J5nDYQAZasDszW0htmlQTkzwEAnDhWchBcjeXWM9bHR/v5z+W01pjNAQAAEMUoOQguf8mJ9fHRfoefywEAAEBQUHIQXI27rSclx5LESg4AAECwUXIQXP6Sk5JnNke4GFzJYYw0AABAsFByEFxsVzsSZ3IAAACCjpKD4PG4D42PpuRYWMkBAAAIOkZII3haqiSvW3ImSgnpptOEB/9KTlejNUo6PtlsHgBBxyhqAAg9VnIQPP7zONkjrHtyIMUlSvGp1uvWvWazAAAARCm+80TwNB1WcmCx2aSMYus1Y6QBAACCgpKD4PEPHcii5BwhfaDktHIuBwAAIBgoOQgef8nJHmk2R7hhJQcAACCoKDkInsEzOZScI2QMHB5mjDQAAEBQUHIQHP3dhw7WU3KOlM5KDgAAQDAxQhrB0bRHks8aHZ2UZTpNePGv5HBXDhBV+txebappUU1zl/o8XvW5Bz48XtltNo0rSNPU4gylJcaZjgoAUY+Sg+A4/DyOzWY2S7jxr+S0H5DcfZIz3mweAKelubNPq/Y0am1ls7r7Pcf8uuqmLr3zSa1GDUnR9NIsjctPldPBhgoACAZKDoKDyWrHlpwrOVySp1dq2ydllZlOBOAUVDV2atmuBm070CbfwOeykuM1uShdSfFOxTvt1ofDrvYetzZUN6uqqUs7D3Zo58EOJcY5NG/8EJ1VliUbPwwCgICi5CA4GnZZz5zRZnOEI7tdSi+y7hFqraHkABGmz+3VW58c0KqKpsHPjcxL0TnDszU6P1X2YxSWM8uy1NDRq/VVzdpQ06LW7n69tmm/yus6dO3UoUpy8U8yAAQKf6MiOBr9JYehA0eVUWyVHIYPABGluqlLf1hbo8bOPknS9NJMzRmZoyFpCSf0+3NSXLpoQr7mjR+ilbsb9dYntdp2oE0PtnTruhnFKstJDmZ8AIgZlBwEns8nNey0XrOSc3SMkQYiitvr1fvb6/TBjnr5JKUnxunaaUUamZdySn+e3WbT7JE5KstJ1otrqtXQ0acnllXogrF5umBMnhx2tq8BwOmg5CDwOhuknlZJNilruOk04SmdCWtApGjs6NULa6q1v6VHkjSlOENXnFGoxHjHaf/ZhRmJWnDBSL226YDWVzfrve11qm7q0vxZJXI5T//PB4BYxVgXBJ5/FSejRIpLNJslXGX478qh5ADhbHttmx7+oFz7W3qUFO/Q188s0XUzigNScPxcToe+Mr1I180oVrzDrvK6Dj350R519bkD9h4AEGsoOQi8wfM4o8zmCGf+MdJsVwPCktfr07vbDuqZlVXq6feqJCtJd31hlCYNTQ/ae04pztC35pQpMc6hmuZuPba0Qm3d/UF7PwCIZmxXQ+CsXWw9P/2z9fT0H/ocjjR4Jmef5PVIdralAOGitbtf/+eljXp3e50k6azhWbp0UoGc9uD/XLA4K0nfnjtci5fvUV17rx5dulu3zi5Tdoor6O8NANGElRwEXof1jYFS8szmCGepBZLNIXn7pfZa02kADNh2oE1XPvSR3t1eJ6fdpq9MK9KVk4eGpOD4DUlL0HfmjlB2cryau/r12NIKHWjtDtn7A0A0oOQg8PwlJ5mSc0wOp5Q21HrNljUgLPzvur265nfLVdXYpaEZifrOeSM0rTTTSJbM5Hh9e+5w5aclqL3XrceXVWhjTYuRLAAQiSg5CCyPW+pqtF6zkvP5BocPUHIAk3r6PfrJHzfrh3/YpJ5+r+aOztVr35+joRlmB6ekJsTp9nOHqyQrST39Xt34xGp9vKfp+L8RAEDJQYB11UvySc4EyZVmOk14GzyXw4Q1wJTqxi5du2iFXlxTI5tNumfeaD31zZnKSo43HU2SlBjv0C2zh2l4TrI6et26+cmP9dGuBtOxACDsUXIQWIdvVbNxmd3nSmclBzDpra21uvzBZfpkf5syk+L09C1n6gfzRskeZhdxupwO3XzOMJ0/Jlfd/R7d+vQavbf9oOlYABDWKDkILIYOnDjuygGM6Oh160d/2KTvPrdObT1uTS3J0F/uOldzR+eajnZMcQ67Hr1pui6eMER9bq++/cw6vbHlgOlYABC2gl5yfvWrX8lms+nuu+8O9lshHHQM/HSRknN83JUDhNzayiZd8pul+sO6vbLZpO+cN1wvfftsFRo+f3MiXE6HHrphmq6cXCi316c7l6zXktX8kAQAjiao9+SsWbNGjz76qM4444xgvg3CSad/JWeI2RyRwH8mp6VG8vnY3gcEUZ/bq9+8u1OLPtgtr08ampGo+6+brFnDs01HOylxDrv++2tTlBTv0ItravRPL2/RgdZu/Z8LR8vG3yEAMChoKzkdHR2aP3++Hn/8cWVmHnsEZ29vr9ra2o74QITy+RgffTLSi6ynu/vQRDoAAbftQJuu+d1yPfy+VXC+PG2o3rz73IgrOH4Ou00LvzxJd88bJUl68L1y/fAPm9Xv8RpOBgDhI2glZ8GCBbrssss0b968z/26hQsXKj09ffCjuLg4WJEQbL1tkrtHkk1KDt+97WHD6ZJS8q3XLVVmswBRqM/t1QN/26krHvxIn+xvU0ZSnH43f5ruv26K0hLiTMc7LTabTXfPG637rp0kh92mP67fq1ufWqOOXrfpaAAQFoJScl588UWtX79eCxcuPO7X3nvvvWptbR38qKnhfELE8p/HSc6xLrvE8XFXDhAUW/e16qqHl+uBv+2S2+vTReOH6J175urSSQWmowXU12aW6IlvzFBinEPLdjXoukdWqra1x3QsADAu4CWnpqZGP/jBD/T8888rISHhuF/vcrmUlpZ2xAciVHut9eQ8zolj+AAQUP0er+7/605d/fBybTtgjYb+7den6tGbpisv9fj/JkWiC8bm6aXvnKWclHh9eqBNlz/4kdZUcmkogNgW8JKzbt061dXVadq0aXI6nXI6nfrwww/129/+Vk6nUx6PJ9BviXAxOFmNknPCDh8+AOC07Gno1FcWrdBv37VWby6dlK937jlPV04ujPpD+WcUZejlO2ZrbH6qGjp69fXHVunZVVXy+XymowGAEQHfU/TFL35RW7ZsOeJzt9xyi8aOHasf//jHcjgcgX5LhAt/yUml5Jww7soBTpvP59Pv19bo3177VF19HqUlOPWLaybpismFpqOFVHFWkv50xzn60f9u1l82H9BP/7xVW/e26t+vniCXk397AcSWgJec1NRUTZw48YjPJScnKzs7+zOfR5QZ3K6WbzZHJMkYZj0ZPACckubOPv3kT5v19ifWD1nOGp6l+6+bEhH33gRDUrxTD319qiYWpus/396ul9bWaMfBdj1y43Tlp1vb9QJ5t84Ns0oC9mcBQCAF/TJQxIiuJqmvw3rNRaAnLrPUejZXWSO4AZyw9dXN+tJvlurtTw4qzmHTvZeM1ZLbzorZguNns9n0vfNH6KlbzlRaglMba1p0xUMfaV1Vs+loABAyIRmB9cEHH4TibWBSw07rmZAhOaPzcG9Q+AcP9Hdad+Uk55jNAxh0MisMG2ta9Kf1e+X2+pST4tLXZhYrNSFOL66pYXVhwHmjc/Xa9+fo9mfWaufBDn39sVX6j6vZUQEgNjDnF4FRv916ch7n+NYuPvLXrjTrjqEVD0kX/quRSECk8Pp8enfbQb2/o16SNC4/VdfNLObMyTGUZifrT3fM1j/8fqPe/uSg/vGPm3XW8GxdNqlADnt0D2MAENvYrobAqB9YyWGy2slLGrh1vbvRbA4gzPW5vXrx4+rBgjN3VI7mn1VKwTmOFJdTi+ZP1z3zRkuSVlU06snle7g4FEBUo+QgMBp2WE+GDpy8pCzr2cW9FsCxtPX06/FlFdq6v00Om03XTivSlyYWyB7lo6EDxW636QfzRunRm6Yr3mnXnoZOPfrhbrV09ZmOBgBBQclBYLCSc+r8KzmUHOCo2nr69cSyCu1r6VZSvEPfmlOm6aWZpmNFpIsn5Ot7541QRlKcGjv79NjSCjV29JqOBQABR8nB6evtkFoHDgxzJufkJQ6s5LBdDfiM9p5+PbFsjxo6+pSRGKfvnTdCw3KSTceKaEPSEvTtc4crJyVeLd39emxphQ629ZiOBQABxeABnL7GXdYzPlmKTzGbJRKxkgMc1aGC06v0xDjddu5wZSXHm45lTCDvt8lIitft5w7X4uWVqm3r0ePLKnTL7DINjfHx2wCiByUHp29wqxrncU6J/0xOd5Pk9Up2FliB9p5+PfHRHtUPFJzbT6LgBLIMRLPUhDjddm6ZnlpRqb3N3XpiWYW+ec4wlWazUgYg8vHdFE5f/TbryXmcU5OQIdnsktcjtR8wnQYwrr2nX//z0R7Vtw+s4Mwpi+kVnGBKPI3YGQAAIABJREFUinfq1tllGpadrF63V0+tqGTrGoCoQMnB6avz35FTYDZHpLI7pMSBQ9QtVWazAIb1uj16ekWl6tp7lZbg1G1zypSd4jIdK6olxDn0zXOGDRadZ1dVqYvx0gAiHCUHp8+/kpPKdrVT5h8+0EzJQezyeH168eMa7W/tUXK8Q7edO5yCEyLxTrvmzypRZlKcmjr7tGRNtTxen+lYAHDKKDk4PX2dUnOl9ZqVnFPnHz7ASg5ilM/n01+27NeOg+1y2m36xtnDlEPBCalkl1M3nTVM8Q67Kuo79cYWts8CiFyUHJye+oFLQJNyJBeT1U5ZEis5iG3/89Eerapokk3SdTOKVZyVZDpSTMpPT9B1M4okSSsrGrWmkqmPACITJQenp37gPE7eOLM5Ih0rOYhhb22t1S/esLa9XjIxXxOHphtOFNvGF6Zr3jhrkMyrG/ersqHTcCIAOHmUHJyeuoHzOLljzeaIdKzkIEZtrGnR3S9tkM8nzSrL0uyROaYjQdIFY3I1cWi6PD6fnl9dpfaeftORAOCkUHJweljJCYzEgZWctn2Su89sFiBEalt7dNvTa9XT79UFY3J1+RmFstlspmNBks1m01emFakgPUGdfR69umm/6UgAcFIoOTg9dZScgHClSvY4ST6ptcZ0GiDoevo9+s5z69TQ0aux+al66IZpctgpOOEk3mnXV6YXyW6TPtnfpq37Wk1HAoATRsnBqettl1oHbhZnu9rpsdmk5IFtOk17zGYBgszn8+lnr2zVppoWZSTF6bGbZijZ5TQdC0dRkJ6ouaNzJUmvbdqv7j6P4UQAcGIoOTh1/slqKUMOnSnBqUu2vpFQU4XZHECQPbuqSr9fu1d2m/TQ16epJJtJauHsgjF5yklxqb3XrTe3MlYaQGSg5ODU+YcOsFUtMJL8Kzm7zeYAgmh1RaP+/bVPJUk/uWSs5oxi0EC4i3PYde20obJJWlvVrPK6DtORAOC4KDk4df6hA7mUnIDwr+Q0UnIQnfa3dOuO59fL7fXpysmFuv3c4aYj4QSVZidr1nBrxf7PG/epz+01nAgAPh8lB6ducCWH8zgBMXgmh+1qiD49/R5997l1auzs0/iCNN137RlMUoswF4/PV3pinJo6+/S3bQdNxwGAz0XJwakbvCOHlZyA8K/ktFRJHrfZLEAA+Xw+/cuft2rz3lZlJsXp0ZumKzHeYToWTpIrzqGrpwyVJC0vb9De5i7DiQDg2Bhng1PT1SS1D9ybwJmcwEhIk5wJkrvHKjrZI0wnAk7IktXVn/u/r6po1Kub9ssm6ZqpRVq2qyE0wRBwY/JTNbkoXZv2turNrbX60cVjWJEDEJZYycGpqbMODiuj1PrmHKfPZpeyBs4oMEYaUaKqsVN/2WxN5Lp4Qr5G5qUYToTTdfGEfDntNu1p6NQHO+tNxwGAo6Lk4NQc/MR6DplgNke0GSw5DB9A5Gvr6deS1dXy+HyaODRd5zJJLSpkJMXr7BHZkqT73twuj9dnOBEAfBYlB6fm4FbrSckJrMGSw/ABRDa316slq6vV3utWXqrLGkHMtqaocd7oXCXE2bW9tl2vbNxnOg4AfAYlB6eGlZzg8Jccxkgjwr2x5YCqm7rkctp146xSuZwMGogmSfFOnTc6T5L0/97ZqZ5+j+FEAHAkSg5Ontd72PhoSk5A+YcNsF0NEWxdVZNWVTRJkr42o1g5qS7DiRAM54zIVn5agva1dOu5VVWm4wDAESg5OHnNe6T+LmsSWBaX+QWU///PlmrJ0282C3AKqpu69OeN1uTFL4zN09gCBpNEqziHXfdcOEqS9PD75Wrr4e8sAOGDkoOT59+qljtWcjCFPKBSC63y6HVbRQeIIG3d/Xp+dZU8Xp/GF6TpC2PzTEdCkF07rUgj81LU3NWvxz7kLCGA8EHJwYlbu9j62PCc9WtH/KHPrV1sNlu0sNulzDLrNcMHEEH6PV49v7pK7T3WoIGvTi+SnUEDUc/psOsfLx4jSXriowrVtfUYTgQAFkoOTl67deeF0grN5ohWg+dyKDmIDD6fT69s3K+a5m4lxjl001mlcsUxaCBWXDh+iKaXZqqn36sH3ys3HQcAJFFycCrarP32lJwgYcIaIszKikatr26WTdL1ZxYrO4VBA7HEZrPpRwOrOS+trWE1B0BYoOTg5Lh7pa5G63UqJScocqyDvGrYaTYHcAJWlDfojS3W6u4lE/M1Ki/VcCKYMKssSzNKM9Xn9urxZaxCAzCPkoOT035Akk9ypUquFNNpolPuWOtZv91sDuA49jR06nvPr5fXJ00pztDskTmmI8EQm82mBV8YKUl6fnW1mjv7DCcCEOsoOTg5/q1qrOIET85o69l+QOpuMZsFOIbWrn5966k1au3uV3Fmoq6ZOlQ2Bg3EtPNH52ri0DR19Xm0ePke03EAxDhKDk5O617rmT7UbI5olphxqETW7zCbBTiKfo9XC5asV0VDpwrTE3TjWaWKc/DPSayz2WxacL61mvPUikq1c28OAIP4Vwknp81fcorM5oh2udYhXrasIRz9/PVP9VF5g5LiHXri5plKTYgzHQlh4uIJ+RqRm6y2HreeXVVlOg6AGEbJwYnzeqQ2//hoSk5Q5Y2znqzkIMw8s7JSz6y0vnn9769N0fjCNLOBEFbsdpvuGFjN+Z9le9Td5zGcCECsouTgxHXUSd5+yeGSkjlgHFSDKznbzOYADrNsV73+7bVPJUn/+KUxunhCvuFECEdXTilUUWaiGjv79OKaatNxAMQop+kAiCBtNdYzfahkox8HVS4rOQiuJatP7pvPuvYePfLhbnm8Pk0tzlB6QtxJ/xmIDXEOu7573gj9y5+36rGlFZo/q1TxTv7NABBa/K2DE9e6z3qyVS34/Cs5bfuknjazWRDzunrdemZllXr6vSrJStLVTFLDcXxlepHyUl060NqjP63fazoOgBjESg5OXCtDB0ImMUNKLbDGSNfvkIpnmk6EGOX2evXc6mo1dfYpMymOSWo4wuet5s0ozdQbW2v167d3yO31yf45xfiGWSXBiAcghvEvFU6M12utKkiUnFBhwhoM8/l8emXjflU2dsrltOsbZw9TioufjeHEzCzLUmKcQ42dffp0PyvSAEKLkoMT07xHcvdIdqeUMsR0mtgweC6HkgMzPipv0LqqZtkkXT+zREPSEkxHQgRxOR2aNTxLkjW0wufzGU4EIJZQcnBiajdbz9QCye4wmyVWsJIDg7YdaNNbW2slSZedUaAx+amGEyESnT08W067TTXN3apq7DIdB0AMoeTgxBzYZD3ZqhY63JUDQw60duulNTXySTqzLEtnD882HQkRKjUhTlNLMiRZqzkAECqUHJwYSk7o+VdyWmuYsIaQ6ei1bqrv83g1IjdZV5xRyCQ1nJY5I3Nlk7Sttl117T2m4wCIEZQcHJ/PJ+3fYL1OLzabJZYkZkppQ63XB7eazYKY4PZ6tWR1lVq6+pWdHK+vn1kih52Cg9OTm+rS2II0SdJHuxoMpwEQKyg5OL6mCqm72Ro6kFZoOk1sKZhiPfdvNJsDUc/n8+m1TftV2dgll9Oum84qVVI8k9QQGHNH5UiSNtS0qL2n33AaALGAkoPj27vGeqYXWUUHoVMw2Xr6twsCQbKyolFrKv2T1IqVxyQ1BFBpdrJKspLk8fq0cnej6TgAYgAlB8fnLzkZpWZzxIK1i4/86Bw4qFvxgfVrIAjK6zr0xpYDkqQvTczXmPw0w4kQjc4dWM1ZvadJvW6P4TQAoh0lB8e3d631zBxmNEZM8p+B6jgouXvNZkFUauzo1QsfV8vrk6YWZ2jOyBzTkRClxhWkKTs5Xt39Hq2tbDYdB0CUo+Tg8/V3Hzr0zkpO6CWkSa40ST6pbZ/pNIgyvW6Pnltdpe5+j4ozE3X11KFMUkPQ2G02nTsqV5K0vLxBHi+XgwIIHkoOPt+BTZLXLaUMsaZ9IfT8qzmte83mQFTx+Xz64/p9OtjWq1SXU/NnlSrOwT8JCK6pJRlKdjnV0t2vLftaTMcBEMX4Fw2fz38eZ+gMiZ/wmuG/m6i1xmwORJVluxq0dV+rHDabbphVorTEONOREAPiHHadM8K6XHbpzgb5fKzmAAgOSg4+n/88TtEMszliGSs5CLClO+v19ie1kqTLJxeoNDvZcCLEkrPKshXvsKu2rUe76jpMxwEQpSg5+HyDJWem2RyxzL+S014r9XWZzYKIV93Ype+/sEE+STNKM3XmsCzTkRBjEuMdmjnM2v68dGe94TQAohUlB8fWdkBq2yvZ7FLhVNNpYldCuhSfIsknHfzEdBpEsK4+t7797Fq1dverKDNRV0wuZNAAjJg9Mkd2m1TR0Km9zfzwBkDgBbzkLFy4UDNnzlRqaqry8vJ09dVXa8eOHYF+G4RCzWrrmTdecqWYzRLLbLZDW9YObDSbBRHL5/Pp3j9t0fbaduWkuBg0AKMykuI1uShDEqs5AIIj4P/Cffjhh1qwYIFWrVqlv/71r+rv79dFF12kzs7OQL8Vgq1ymfUsnW02B6SMgZLj3z4InKRnVlbplY375bDb9PANU5XOoAEYdu5oa5z0J/vbtKeB7xEABJYz0H/gW2+9dcSvn3rqKeXl5WndunWaO3duoN8OwbRnoOSUnWs2B6TMMutZvcJsDkSkdVVN+vnrn0qS7r1krGYNz9buer6phFn5aQkaMyRVOw626/FlFfrlNZNMRwIQRYK+V6G1tVWSlJV19MOtvb29amtrO+IDYaD9oNSwQ5KNlZxwkFkmySa1VEutXAqKE1ff3qs7nl8vt9eny84o0LfmlJmOBAyaO7Ca87/r9qquvcdwGgDRJKglx+v16u6779bs2bM1ceLEo37NwoULlZ6ePvhRXFwczEg4Uf6tavkTpSSmLxkXlyClD7VeV680mwURw+3x6vsvrNfBtl6NzEvRfdeewaABhJVh2UkqzkxUn9urp1dUmo4DIIoEteQsWLBAW7du1YsvvnjMr7n33nvV2to6+FFTw4WHYcFfcoaxxTBsZA23nlVsWcOJ+fXbO7SqoknJ8Q49cuN0pbgCvkMZOC02m03nDazmPLuySh29bsOJAESLoJWcO++8U6+//rref/99FRUVHfPrXC6X0tLSjvhAGOA8TvjJGmE9WcnBCXhr6wE9urRCkvTrr07WyDwmJCI8jS1I0/DcZLX1uPXMykrTcQBEiYCXHJ/PpzvvvFMvv/yy3nvvPZWVsf874rTtl5p2W/fjlJ5jOg38/Cs5dZ9KXU1msyCs7a7v0A//sFmSdPu5Zbp0UoHhRMCx2W023XnBSEnSE8v2qJPVHAABEPCSs2DBAj333HNasmSJUlNTVVtbq9raWnV3dwf6rRAs/lWcginWRZQID65UKdv6RmDwDiPg73T2uvXdZ9epo9etWWVZ+vGXxpqOBBzXlZMLNSw7SU2dfXpuVZXpOACiQMBLzqJFi9Ta2qrzzz9fBQUFgx8vvfRSoN8KwVK51HqyVS38lJxtPTmXg6Pw+Xz68R83a1ddh4akufTQDdPk5MJPRACnw64FA6s5jy2tUHefx3AiAJEuKNvVjvbxzW9+M9BvhWDweqVdf7Nel51nNgs+y799kHM5OIrFyyv1+uYDctpt+t38acpNdZmOBJywq6cOVUlWkho7+/T8alZzAJwefsSHI+3fIHXUSvGp0rA5ptPg7/lLzv4NUm+H2SwIK2sqm/TLN7ZJkv7lsnGaXsrod0SWOIddCy6wBqw88mGFevpZzQFw6ig5ONKON6znyC9KTn4KHHYySq0Pr1vas9R0GoSJuraewQs/r5pSqJvPGWY6EnBKvjytSEMzEtXQ0aslq6tNxwEQwSg5OJK/5Iy9zGwOHJ3NJo260Hpd/jezWRAW+txeLViyXvXtvRozJFULvzyJCz8RseIOO5vzyIe7Wc0BcMooOTikaY81ntjmOPSNNMLPSH/J+avk85nNAuP+4y+fak1ls1JdTi26cZqS4rnwE5HtK9OLVJieoLr2Xr20hgvCAZwaSg4O8a/ilJ4jJWaazYJjKztXcsRLLdVSwy7TaWDQ79fW6JmVVbLZpAeun6LhuVz4icgX77TrewOrOYs+2K1eN6s5AE4eP/LDIdvZqhYR4pOl0tlSxfvWak7uaNOJEEL+cwo1TV16bFmFJOmLY/N0sI0zDIge180o0sPvlau2rUcvrK7WN2dzsTiAk8NKDixdTYfGEo+51GwWHN/IedZz11/N5oAR7T39en51lTxen8YXpOn8MXmmIwEB5XI6dOcXrNWc375XrraefsOJAEQaVnJg+eRlyeeR0gql3e+ZToPjGXWh9M4/S1XLpb5Oa3UHMcHt9eqFj6vV1uNWbopLX5leJDuDBhCFrp9ZrMXL92h3fad+9/5u/eSSsaYjAYggrOTAsnGJ9SyaaTYHTkzOaCm9RPL0SXuWmU6DEHpjywFVNnbJ5bTrxrNKlRDnMB0JCAqnw65/unScJOnJ5Xu0t7nLcCIAkYSSA6l+p7RvrTVVbegM02lwIg4fJe0fGIGo9+yqKq2qaJIkfW1GsXJTucsK0e0LY/N09vBs9bm9+q+3d5iOAyCCUHIgbRpYxRl1keRKNZsFJ27cFdZz++uSx202C4Ju2a56/eurn0iSLho/RGML0gwnAoLPZrPpny8bJ5tN+vPG/dq8t8V0JAARgpIT67weadOL1uspXzebBSdn2LlSUrbU1ShVsmUtmpXXdeiO59fL4/VpanGGzhudazoSEDITh6brmqlDJUm/+Ms2+bgfDMAJYPBArFm7+Mhf122T2g9IcUlSe53k4D+JiOFwSmMvl9Y/LX36Z2nEBaYTIQiaO/t029Nr1N7j1ozSTF05uVA2Bg0gxvzwojH6y+YDWr2nSX/bVqcLxw8xHQlAmGMlJ9bVrLaeQ6dTcCLRhGus57bX2LIWhfrcXn3v+XWqbOxSUWaiHr1pupwO/tpG7CnMSNRt51p35Sx8Y5v6PV7DiQCEO/61jGXdLVLtZut1yVlms+DUHL5lreoj02kQQD6fTz97ZatWVTQpxeXUk9+cqewUBg0gdn33vBHKSYlXRUOnnl9VZToOgDBHyYllVR9JPq+UNUJKG2o6DU6Ff8uaJH3yZ7NZEFAPv1+uF9fUyG6THrxhqkYPYSgIYltqQpzuuXC0JOm/3tmpfS3dhhMBCGfsT4pVnj6paqX1uuw8s1lw4v7+TJVknaeSpG2vSpf+WnLEhTYTAu73a2r0X+/slCT965UTdMGYPMOJgPDw9Zklenn9Pq2tatY//WmLnrplJmfUABwVJSdW7Vsv9XdKiZlS/kTTaXA6skdKrjRry9rOt6Vxl5tOhNPw7raDuvflLZKkO84foW+cPcxsICAElqyuPuGvPXdUrjbWtOjDnfX60R82a1pp5uD/dsOskmDEAxCB2K4Wi3w+ac+H1uth50o2/jOIaPbDLnHd+LzZLDgt66ubtWCJNSr62mlF+tHFY0xHAsJObqpL88ZZ09Ve37JfbT39hhMBCEd8dxuLGndZY6Md8VIxAweiQvGZ1nPn21L7QbNZcErK6zp061Nr1NPv1fljcvWrayexDQc4htkjczQ0I1E9/V69snE/d+cA+AxKTiza/a71LJ4lxSeZzYLASM2Xis6UfB5p80um0+Ak1bb26OYnP1ZLV78mF6Xrd/OnKY5R0cAxOew2XTutSA6bTdsOtGnLvlbTkQCEGf4VjTWte6X6HZJs0vDzDYdBQE2dbz03PGdtSUREqGvv0Q2Pr9K+lm4Ny07Sk9+cqaR4jksCx5OfnqDzx+RKkl7dtF8dvdwVBuAQSk6sqXjfehZOse5XQfSY8GXJmSg17JD2rjWdBiegoaNXNzy+WhUNnRqakahnvzWLu3CAk3DemFzlpyWoq8+jVzbuY9sagEH8uDCWtFRL+zdYr0d8wWwWBF5CmjThamnTC9KaJ6TimaYT4TB/Pz2qs9et//loj2rbepSW4NT1M4u1bFeDoXRAZHLa7bp2WpEWfViuT/a36ZEPK/S980eYjgUgDFByYsmqRdblnzmjpfRi02kQaGsXS6kF1ustf5DyxkkJ6davZ9xiLhc+o6vPrSeXWwUn1eXUbXOGs4IDnKKhmYm6YnKhXtm4X//59naNLUjlbikAbFeLGd3N0rqnrdfDLzCbBcGTUSJlllkDCKpWmE6Do+jp92jx8kodaO1Rssupb80pU04qBQc4HbPKsjVzWJZ8PumuFzaoor7DdCQAhlFyYsXaxdbln6kFUu5Y02kQTGVzrWfVcsnD/RHhpKPXrSeWVWhfS7eS4h361pwy5aUlmI4FRIUrJhdoRmmm2nvcuv2ZtWrn/hwgplFyYoG7V1r9iPV6xAUSd29Et/wzpIQMqa9D2r/edBoMaOnq02NLd2t/a4+S4x26dXaZ8ik4QMA47Xb97sZpyk9L0O76Tt3z0kZ5vQwiAGIVJScWbPmD1HFQSi2UCqeZToNgszukYXOs1xUfMk46DJTXdejRpRVq6OhTRmKcvjN3hAozEk3HAqJOXmqCHvvGdMU77frbtjrd/9edpiMBMISSE+28XmnFg9brs74r2Zk1ERNKzpYcLql9v3Rwq+k0MW3z3hZd9+hKtXb3KzfFpW/PHc4ZHCCIzijK0K++PEmS9ND75Xr4/XLDiQCYQMmJdrvfleq3S/Gp0vRvmk6DUIlPlsrOtV7vfJvVHENW7G7QDY+vVlNnn4ZmJOrbc4crIynedCwg6n15WpH+4cLRkqRfv71D97+zgzt0gBhDyYl2Kx+2ntO+cWicMGLD8POt1Zy2vdKON02niTkvranWN/7nY3X0unX28GzdNqdMyS5WUoFQ+f4XR+knl1iDdn77Xrl+9eZ2ig4QQyg50ezgJ1LF+5LNLs36juk0CLX4lENncz5YyGpOiHi8Pv3H65/qx3/cIrfXp8vPKNDiW2bKFecwHQ2IOd89b4T+7xXjJUmPLq3Qv732KcMIgBhByYlmK39nPcddKWWWms0CM0ZcIDnipdrN0vbXTaeJeu09/brt6TV64qM9kqR75o3Wg1+fqgQKDmDMLbPL9MtrJslmk55aUal/enmL3B6v6VgAgoySE6066qQtv7den73AbBaYE59ibVuTpHd+ao0TR1DUNHXp2kUr9P6Oermcdj10w1T9YN4o2RjZDhh3w6wS/fork2W3SS+uqdHXH1+l2tYe07EABBEbxKPVx49Lnj6paKZUfKbpNDBpxBelA5ul5j3SqkXSnLtNJ4ooS1ZXH/drdtS26w/ratTV51FqglM3nVWqtm73Cf1eAKHxlelFSnE59cM/bNKaymZd+ttluv+6yTp/TJ7paACCgJWcaNTXJa15wnrNKg6cLmne/7VeL/0va5UPAeHx+vTOJ7V6emWluvo8GpqRqDvOH6mizCTT0QAcxZcm5uv178/RhMI0NXX26ZuL1+g/39rO9jUgClFyotGmF6TuJimjRBp7hek0CAdnXG9dBNvXLr37b6bTRIW2nn49uXyPPthZL0maVZal78wdrvTEOMPJAHyeYTnJ+uP3ztE3zrbOqv7ug9264fHVqmrsNJwMQCBRcqKN13tobPRZCyQHOxIhyW6XvvQr6/WG56SKD83miXC76zv00Hvl2tPQqXinXV+bWayrpgyV08FfqUAkSIhz6N+vmqiHb5imFJdTH1c26cL/Xqr739mh7j6P6XgAAoB/kaPNzjelpt3WnThTbzSdBuGkZJY08zbr9avfl3o7zOaJQG6PV29trdWTH+1RR69b+WkJWnD+SE0uyjAdDcApuOyMAv3lrjmaMzJHfW6vfvteuebd/6He2lrLnTpAhKPkRJsVD1nP6bdIrhSzWRB+5v2rlF4itVRJ7/676TQR5WBbjxZ9uFtLd9XLJ2lGaaa+e94I5aa6TEcDcBpKs5P17LfO1KL501SYnqB9Ld367nPr9I0nP9aO2nbT8QCcIkpONKlaKVWvkOxxXP6Jo3OlSlf+xnr98aNsWzsBXp9Py8sb9PD75TrQ2qOkeIfmzyrRl6cVKd7JX6FANLDZbLpkUoH+9g/n6c4LRireYdeyXQ26+IGlWvD8esoOEIFsvjBbj21ra1N6erpaW1uVlpZmOk5kefYaafd7UsnZ0hlfM50G4WzzS1L1SsmVJs39kTT7LtOJwtLe5i7d/OTH2l1vHUgePSRF104rUmoCwwWAcHTDrJKA/DmVDZ26763tenNr7eDnLp2Ur7u+OEpj8/neBAilU+0G/BgyWuxdaxUcm0MaOc90GoS7CddIqQVSb5s1iMDL+NTDeb0+PbV8jy7676XaXd+pOIdNV00p1M1nD6PgADFgWE6yFt04XW/dfa4unZQvSXpjS62+9MAy3f7MWq2qaOTMDhDmWMmJFs9fJ+16W5pyo1Q0w3QaRIL2Wumj+61LYy/4Z+m8fzSdKCyU17Xrx3/conVVzZKk0uwkXTu1SDmcvQFixt+vCG2vbdOD75brja0H5P+uaVxBmm6dPUxXTC5UQpzDQEogNpxqN6DkRIP9G6THzpdsdunOtdKepaYTIVLUfCxtWmK9vu5ZafyVZvMY1Of26tEPd+vB98rV5/EqOd6hn1w6TjZJdpvNdDwAIXSsbW/ldR16asUe/XHdPnX3W6Oms5PjdcOsEl03o1jFWVwEDAQaJSdW+XzS01dIlcusczhffkxau9h0KkSSrX+SKpdKcUnSrW9JBZNNJwq5FeUN+tmrn6i8zhqrff6YXP3imkkampGoJaurDacDEG66+txaW9mslRWNau3ulyTZJI3ITdGMYZkaX5Amp8MesDNCQCw71W7ATZGR7tNXrILjTJC+8C+m0yASjb9KcsRJu9+Vllwv3fZXKb3IdKqQqG3t0S/e2KbXNu2XZP1E9l8uH6erpwyVjdUbAMeQFO/U3NG5mj0yR58eaNOayiaV13WovN76SIxzaErBmGyQAAAStUlEQVRJhsYXpmlyUTp/nwAGUHIiWV+X9M5AsZn9AymDnxjhFNgd0lcXS09cKDXskJ65WrrlTSkl13SyoOn3ePXU8ko98Led6uzzyG6TbjyrVP9w4RilJzFYAMCJcdhtmjQ0XZOGpqups0/rqpq1rqpJbT1urdzdqKsfXq7S7CRdOblQV00p1Mi8VNORgZjBdrVI9sGvpA8WSmlF0p1rpPiBvcBsV8PJmnGL1LpXevJLUmuNlD9Juvl1KTHDdLKA8vl8eufTg7rvre2qGBgLPbUkQz+/aqImDk0/6u9huxqAk+H1+bTrYLs21LRo18GOwbM7kjWs4JKJ+bpowhCNGZLKCg9wAjiTE0vWLpZaqqUVv5G8HmnazVLhVNOpEA066qQVv5X6OqT0YutS2XO+bzpVQGyobtYv39imNZXW1LTs5Hj9+Etj9ZXpRbLbj/2NBiUHwKm6akqh/rbtoF7duF8f7qyX23voW66SrCRdNH6ILpqQr2klGXI6uNUDOBpKTixZ8ZC07L+k7mZpyERpxrckfhqEQGnbL616WOrrlFKGSLe/L6UPNZ3qpPnLSWNHr9759KC27GuVJDntNs0ZmaO5o3MZ+wogZLp63fr0QJs+PdCm8rqOIwpPQpxdI/NSNTovRaOGpCo98chtswwwQCxj8ECs8HqkDc9aBScpR5oyn4KDwEorlM7+vrT6EanjoPTkxdL1S6SCM0wnOymNHb36YEe9NtQ0y+uzJh9NLcnUheOHfOYbCAAItiSXUzOGZWnGsCz1uj3adbBD2w60aXttu7r7Pdq6r1VbB34Yk5+WoJF5KRqek6xhOcmGkwORiZWcSNLfI72yQNr6v5I9Tppzj/UNKRAMXU3S6kVSZ73kTJSu+I00+WumUx1XRX2HHnqvXC9v2Cf/X26jh6To4gn5KkhPNJoNAP6ex+vTvuYu7azr0K6D7drb3K3DvzGzSTqjKF1njcjWWWXZmlaSyYAUxBS2q0W7zgbpxRukmtXWpZ9Tb+IcDoKvr1OqeF8q/5v166k3Shf9IiwHEmyobtbi5ZV6ffN++XeBjBmSqi+MzeOCPgARo7PXrfL6DlXUd6iivlONnX2f+ZpReSmaXpqpaaWZmlaSqeE5yZ97thCIZJScSPR5U9Bm3GI9e1qljx+XVj4sdTdJrnRp6nwpZ3RoMgLTvmFN8Vv6a+vXKfnSpb+Wxl1hfKtkr9ujv2w+oKdXVGrT3tbBz88bN0Sjh6SoKJNyAyCytXb3Kz/dpZW7G7W2slkVDZ2f+ZoUl1MTCtN0RlG6JhVlaNLQdJVmJVF8EBUoOZHo70uO12Odtemsl9IKpKoV1kefdQu7ckZLX3vO+hwQKv7CXbVCevX7UmO59euiM6Uv/lQqmxvySHsaOvWn9Xv1wsfVauiwfsoZ77DrismFunXOME0oTGcqGoCocfjggcaOXm2obtG66matq2zW5n0t6un3fub3JMU7NCY/VeMK0jQuP1VjC9I0+ihDDYBwx+CBSNPTKtVtk5orrftJOuukrkbJ99m/qJQzRpr7Q2nClyWHk5IDM0rPkb673FrRWfmwtPdj6ekrpKHTrQl/E78sxQXvzMvBth69tmm/Xt20X5sPW7XJT0vQTWeX6vqZxcpOcQXt/QEgHGSnuDRv/BDNGz9EkuT2eFVe36HNe63BBZv3turTA23q6vNoQ3WLNlS3HPH789MSNGpIikYPSdXoIdY0t5F5KUpLoPwgurCSE0qt+6Rtr0nbX7eKis/z2a+xx0nJ2VLRTKnkbOujYIpkP2x+Ppd9IpT8KzmHa6+Vlv0/ad1Tkmdgv3h8qjTmEmn8VdLw8yTX6d3s7fP5tPNgh5btqte72+q0ak+j/H9bOQbGQF83o1gXTRiiuKPcL8FKDoBY5fH61NjRq9q2Hh1o7VFta49q23rU2t1/zN+TluBUXmqCctNcykt1KTfVpdwUl1JczqNeWspYa4QK29XCVWejtO0Vacsfparl0uEzU5KypcwyKbPUuo8kOU9KSDd+zgE4Yb3t1jCMqhXWmTE/m13KKJUmXCMVTpHyz5AySiT7391Lc1hh9/mkvV12rW2I07L/397dxzZV7nEA/55z2tNusFdGuw3KxSEyuLyZIc0wJBgncC8Rl2Aky4KYLMHEgUY0Yb7gphjd1f1hEC7ESQLJ1QxYghKCMzj0etFlGnC5IDIycAKbHZuja2Gsb+e5f3Srt2MbdOvWrvt+kuacPn3O6e8sv7Xn1+e8XFdxqjMZ152uoO5L/pKCtYsz8fcFGUi7y6gNixwiomA9Hh+uO3rQ5nChzdmDNkcP2p0uOHq8gy4Tp1f8BU+CAWmTDUibrGLKZAOeWzGL9xqjMRF1Rc7u3bvx/vvvw2azYdGiRfjwww+xdOnSuy4XE0VO52WgqRZoPA5c/nfwiM2MXP8J23P+5n+NKBYIDbD/BrQ2AG3n/Ide9ifrgKTpwGQzvPoE3IIRzk4b7G4FXW4JN90CXiGgQYYXCrqFEbfkSUhMNcOUeR/+Om8ezDOygYT0e/ohgEUOEdG9ue32od3Zg+tOF647XWh3utB+04Ubt9wYbCdRkoDMpDhYUuNgSYmHJTU+MJ+eZIQpwQhVd+coO1GooqrIOXjwIJ5++mns3bsXVqsVH3zwAQ4fPozGxkaYTKYhlx03RY6mAT12wNEC3PjNfzJ2609Ayxmgq9/OVcYi//k0C5707+T14WFnFAM0Adz2Sbjt9U9veSXcdnRC33kRRtGDJPs5pN66BEUM/kthSPTxQGoWkHofkDrLP0KUZPFfrCM+zT9CqlNZ5BARjZDHp6HjZm/R43Thj1vuwHOXd4BziPuZMkmFKdGI9EQDUicZkBKvR8okFcnxeiTHqUiK0yPeoCBeVTBJ1SFeVRCnKlAVGboBDkMOByEEfJqAxyfg9mlwezW4fRo8vVOXR4Pb54PLo8HV+9zT28/j0wLLeLW+9WjwaQJeTUDrvX9B/x1rWZKgyIAiy1B65/WKDFUn+6eKDL1OgqooUHX+dkPvVFX+b14nw6Dz99HJElRFnhBX0IuqIsdqteKhhx7Crl27AACapsFisWDLli0oKSkJ6utyueBy/XlISldXF2bMmIGrV69Gvsj57yHg9AFA8/qvfOZzA97bgLsbcDlwZxr3knSA5SEga4V/xCY1a+B+Z/41SoETDY9XAwr/kwxN+IsXAQm+3nlNAG7N/9yjSfD0Tl2+u3/AytAwFXZMl9qRIt3EZKkbaXoPpujcSI/zwhQnYIoTmGoU0Emaf2Ro6lygxwF0dwDOVv8PCl2tAO7+xQrFgB45Hl7FCJ+shyarEJIMSDIEFHy95J/QFHXkfzAioglICIG8uWb89scttNhvo+VGD67d6PbP22+j3emCxzey3UtZAnSKDFUnQZX9O/Oy5C8YJEmC0rtzL3r3xYToe4je7yx/0aEJ/1ECXt+fxU0sUWQJOkWCXvb/TXS9U1mSoCgSFMk/3//vJ0n+G81K8Lf3HSHR943e9/o/1i3E9Ajfa87hcMBiscButyMpKemelwt7keN2uxEfH4/q6mrk5+cH2jdu3Ai73Y7PP/88qH9ZWRnefPPNcIZAREREREQx5OrVq5g+ffrdO/YK+yWkOzo64PP5YDabg9rNZjMuXLhwR/9XXnkFW7duDTzXNA2dnZ2YMmXKgFfzoNjVV6lHxSgejVvMIxop5hCNFHOIwoF55CeEgNPpRGZmZkjLRfw+OQaDAQZD8FWSkpOTIxQNRYPExMQJ/c9M4cE8opFiDtFIMYcoHJhHCOkwtT5hP6srLS0NiqKgra0tqL2trQ3p6enhfjsiIiIiIqIgYS9yVFVFTk4OamtrA22apqG2tha5ubnhfjsiIiIiIqIgSllZWVm4V5qYmIjt27fDYrHAYDBg+/btaGhowL59+zB58uRwvx3FEEVRsGLFCuh0ET+SksYx5hGNFHOIRoo5ROHAPBq+UbsZ6K5duwI3A128eDF27twJq9U6Gm9FREREREQUMGpFDhERERERUSSMzu1kiYiIiIiIIoRFDhERERERxRQWOUREREREFFNY5BARERERUUxhkUMR1dnZicLCQiQmJiI5ORlFRUW4efPmkP23bNmCOXPmIC4uDjNmzMDzzz+Prq6uMYyaIm337t2YOXMmjEYjrFYrfvjhhyH7Hz58GNnZ2TAajViwYAGOHz8+RpFStAolhyorK7F8+XKkpKQgJSUFeXl5d805in2hfg71qaqqgiRJyM/PH+UIaTwINY/sdjuKi4uRkZEBg8GABx54gN9pg2CRQxFVWFiIn3/+GSdOnMCxY8fw7bffYtOmTYP2b21tRWtrKyoqKnDu3Dns378fNTU1KCoqGsOoKZIOHjyIrVu3orS0FGfOnMGiRYuwatUqXL9+fcD+33//PQoKClBUVISffvoJ+fn5yM/Px7lz58Y4cooWoebQN998g4KCAnz99deoq6uDxWLBypUr0dLSMsaRU7QINYf6NDc34+WXX8by5cvHKFKKZqHmkdvtxmOPPYbm5mZUV1ejsbERlZWVmDZt2hhHPk4Iogg5f/68ACB+/PHHQNsXX3whJEkSLS0t97yeQ4cOCVVVhcfjGY0wKcosXbpUFBcXB577fD6RmZkp3n333QH7P/XUU2LNmjVBbVarVTz77LOjGidFr1BzqD+v1ysSEhLEgQMHRitEinLDySGv1yuWLVsmPv74Y7Fx40bxxBNPjEWoFMVCzaM9e/aIrKws4Xa7xyrEcY0jORQxdXV1SE5OxpIlSwJteXl5kGUZ9fX197yerq4uJCYm8m7AE4Db7cbp06eRl5cXaJNlGXl5eairqxtwmbq6uqD+ALBq1apB+1NsG04O9dfd3Q2Px4PU1NTRCpOi2HBz6K233oLJZOKRBwRgeHl09OhR5Obmori4GGazGfPnz8c777wDn883VmGPK9wrpIix2WwwmUxBbTqdDqmpqbDZbPe0jo6ODuzYsWPIQ9wodnR0dMDn88FsNge1m81mXLhwYcBlbDbbgP3vNccotgwnh/rbtm0bMjMz7yieaWIYTg6dOnUK+/btQ0NDw1iESOPAcPLo8uXLOHnyJAoLC3H8+HE0NTXhueeeg8fjQWlp6ViEPa5wJIfCrqSkBJIkDfm4152JoTgcDqxZswbz5s1DWVnZyAMnIrqL8vJyVFVV4ciRIzAajZEOh8YBp9OJDRs2oLKyEmlpaZEOh8YxTdNgMpnw0UcfIScnB+vXr8drr72GvXv3Rjq0qMSRHAq7l156Cc8888yQfbKyspCenn7HyXVerxednZ1IT08fcnmn04nVq1cjISEBR44cgV6vH2nYNA6kpaVBURS0tbUFtbe1tQ2aM+np6SH1p9g2nBzqU1FRgfLycnz11VdYuHDhaIZJUSzUHLp06RKam5vx+OOPB9o0TQPgP3qhsbERs2bNGt2gKeoM57MoIyMDer0eiqIE2ubOnQubzQa32w1VVUc15vGGIzkUdlOnTkV2dvaQD1VVkZubC7vdjtOnTweWPXnyJDRNg9VqHXT9DocDK1euhKqqOHr0KH9NnUBUVUVOTg5qa2sDbZqmoba2Frm5uQMuk5ubG9QfAE6cODFof4ptw8khAHjvvfewY8cO1NTUBJ1HSBNPqDmUnZ2Ns2fPoqGhIfBYu3YtHnnkETQ0NMBisYxl+BQlhvNZ9PDDD6OpqSlQJAPAxYsXkZGRwQJnIJG+8gFNbKtXrxYPPvigqK+vF6dOnRKzZ88WBQUFgdevXbsm5syZI+rr64UQQnR1dQmr1SoWLFggmpqaxO+//x54eL3eSG0GjaGqqiphMBjE/v37xfnz58WmTZtEcnKysNlsQgghNmzYIEpKSgL9v/vuO6HT6URFRYX45ZdfRGlpqdDr9eLs2bOR2gSKsFBzqLy8XKiqKqqrq4M+c5xOZ6Q2gSIs1Bzqj1dXIyFCz6MrV66IhIQEsXnzZtHY2CiOHTsmTCaTePvttyO1CVGNh6tRRH3yySfYvHkzHn30UciyjHXr1mHnzp2B1z0eDxobG9Hd3Q0AOHPmTODKa/fff3/Qun799VfMnDlzzGKnyFi/fj3a29vxxhtvwGazYfHixaipqQmcvHnlyhXI8p+D1MuWLcOnn36K119/Ha+++ipmz56Nzz77DPPnz4/UJlCEhZpDe/bsgdvtxpNPPhm0ntLSUp4POEGFmkNEAwk1jywWC7788ku8+OKLWLhwIaZNm4YXXngB27Zti9QmRDVJCCEiHQQREREREVG48GcGIiIiIiKKKSxyiIiIiIgoprDIISIiIiKimMIih4iIiIiIYgqLHCIiIiIiiikscoiIiIiIKKawyCEiIiIiopjCIoeIiIiIiGIKixwiIiIiIoopLHKIiIiIiCimsMghIiIiIqKY8j/w4DPYwhkppQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight = []\n",
    "for _ in range(800):\n",
    "    # index = 1 corresponds to \"linear.weight\"\n",
    "    weight.append(pred_loaded(x_data)[1])\n",
    "weight = torch.stack(weight).detach()\n",
    "weight = weight.reshape(weight.shape[0], 3)\n",
    "gamma_within_africa = weight[:, 1] + weight[:, 2]\n",
    "gamma_outside_africa = weight[:, 1]\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "sns.distplot(gamma_within_africa, kde_kws={\"label\": \"African nations\"},)\n",
    "sns.distplot(gamma_outside_africa, kde_kws={\"label\": \"Non-African nations\"})\n",
    "fig.suptitle(\"Loaded TorchScript Module : log(GDP) vs. Terrain Ruggedness\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next section, we'll look at how to write guides for variational inference as well as compare the results with inference via HMC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "  1. McElreath, D., *Statistical Rethinking, Chapter 7*, 2016\n",
    "  2. Nunn, N. & Puga, D., *[Ruggedness: The blessing of bad geography in Africa\"](https://diegopuga.org/papers/rugged.pdf)*, Review of Economics and Statistics 94(1), Feb. 2012"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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